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Article

Identification and Evaluation of Flesh Texture of Crisp Pear Fruit Based on Penetration Test Using Texture Analyzer

by
Yulu Mou
1,
Xingguang Dong
1,*,
Ying Zhang
1,
Luming Tian
1,
Hongliang Huo
1,
Dan Qi
1,
Jiayu Xu
1,
Chao Liu
1,
Niman Li
2,
Chen Yin
1 and
Xiang Yang
1
1
Research Institute of Pomology, Chinese Academy of Agricultural Sciences (CAAS), Key Laboratory of Horticulture Crops Germplasm Resources Utilization, Ministry of Agriculture and Rural Affairs, Xingcheng 125100, China
2
School of Software, Liaoning Technical University, Huludao 125105, China
*
Author to whom correspondence should be addressed.
Horticulturae 2025, 11(4), 359; https://doi.org/10.3390/horticulturae11040359
Submission received: 6 February 2025 / Revised: 16 March 2025 / Accepted: 18 March 2025 / Published: 27 March 2025
(This article belongs to the Special Issue Fruit Tree Physiology and Molecular Biology)
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Versions Notes

Abstract

:
Flesh texture is an important quality trait and is related to people’s preference for fruit, especially for crisp pears. Puncture tests were carried out on 156 crisp pear fruit germplasm samples to analyze the diversity level of texture traits, to clarify the correlation between sensory description evaluation and instrumental traits, and to explore the effects of fruit ripening, size, and shelf life on the change in flesh texture. The results showed that puncture parameters were significantly different between crisp pear cultivars, and the work associated with the flesh limit compression force had the highest coefficient of variation (0.281). There was a significant correlation between puncture parameters and sensory evaluation scores. The correlation between sensory score and flesh firmness was the highest, with a correlation coefficient of 0.708, indicating that hardness can significantly influence the sensory evaluation of texture. Cluster analysis based on sensory evaluation and puncture determination could divide the germplasm resources of crisp pear into five texture categories: loosen, crunchy, crisp, tight–crisp, and dense. A comprehensive texture score model, constructed by principal component analysis, showed consistency with sensory evaluation scores and proved that the combination of a puncture test and sensory evaluation is the best way to identify and evaluate the texture of crisp pear. Further analysis of the influencing factors of flesh texture showed that fruit maturity and shelf life had significant effects on flesh quality. This study provides an important reference for the standardization, evaluation, and utilization of crisp pear variety resources.

1. Introduction

Pear (Pyrus spp.) is a globally significant fruit and one of the most extensively cultivated species in temperate regions. As the primary center of origin for pears, China boasts an exceptionally rich diversity of pear germplasm resources, encompassing 13 native species. Notably, the predominant cultivated species, including P. bretschneideri, P. pyrifolia, and P. ussuriensis, all originated from China. Pears demonstrate a wide geographical distribution and have diversified into numerous locally adapted cultivars in response to diverse environmental conditions [1,2]. Texture is a critical quality attribute of fresh fruits, alongside appearance and flavor; some people like soft pears but others like hard pears, and some pears that need after-ripening have a better flavor when they are fully after-ripened, with texture significantly influencing consumer acceptance [3]. The diversity in flesh texture among pear varieties is considerable, and consumer preferences for specific textures play a pivotal role in the fruit industry’s development [4,5,6,7]. Changes in texture are intricately associated with physiological processes, profoundly influencing crucial quality parameters such as sensory characteristics, storage potential, and processing suitability [8,9]. Therefore, studying the changes in flesh texture can help in improving the storage technology of pear fruit and diversifying its processing methods.
The flesh texture of pear can be divided into soft-type and crisp-type based on changes in fruit hardness during ripening. Cultivated species such as P. ussuriensis and P. communis belong to the soft type, which is further divided into melting and soft types; cultivated species such as P. bretschneideri and P. pyrifolia belong to the crisp type, which is further divided into crisp, loose–crisp, and tight–crisp texture types according to the tightness of the flesh [10]. According to the statistical analysis of 562 pear germplasm resources preserved in the National Germplasm Repository of pear in China, the crunchy type has the most resources, accounting for 40.57%. Soft-type pear cultivars account for 26.51%, while crisp-type pear cultivars account for more than 60% [11]. Crisp pears occupy a relatively high proportion in the germplasm resources of pears and have rich texture types, which plays an important role in research on the flesh texture of pears.
At present, the evaluation of fresh texture varieties mainly includes two methods: qualitative sensory evaluation and quantitative instrument determination [12,13]. Studies have shown that there is a good correlation between sensory properties such as hardness and crispness and hardness tester measurements [14]. Instrumental analysis offers a rapid and objective approach for evaluating fruit textural properties; however, the complexity and multidimensional nature of texture as a quality attribute cannot be fully captured by instrumental measurements alone. Consequently, while instrumental methods provide valuable quantitative data, they should be complemented with sensory evaluation, which serves as an essential reference standard for calibrating and validating instrumental readings. [15,16]. Sensory evaluation is more complex and subjective and can truly reflect a human’s instant information on texture, and instrumental measurement is more economical, objective, and stable [17]. Therefore, the combination of sensory evaluation and instrument measurement is the best method to identify and evaluate texture properties.
A texture analyzer, also known as a physical property analyzer, is a commonly used instrument in the detection of fruit and vegetable texture characteristics. Puncture tests can be performed on unpeeled or peeled fruit samples to quantitatively determine the texture characteristics of fruits, and they are accurate, objective, and simple [18,19,20]. With the popularity of the texture analyzer, relevant texture evaluation systems have been established for many fruits, and the best test conditions for texture testing have been obtained [21,22,23]. Through puncture testing and texture profile analysis (TPA), the fruit texture traits of apples [24,25], pears [26,27], peaches [28,29], kiwifruits [30], and grapes [31,32] were studied, and most of the research contents focused on the differences and changes in fruit texture traits during storage after harvest. In kiwifruits [33], cucumbers [34], and watermelons [35], there are studies on the identification and evaluation of multiple germplasm resources by a texture analyzer and the comparison of the changes in texture parameters among different germplasm resources. However, there have been no studies on the systematic identification and evaluation of crisp pear varieties based on a texture analyzer and sensory evaluation.
In the literature on instrumental measurement and sensory evaluation, there are no studies focusing on the texture evaluation of crisp pears. Sensory texture descriptors are also more inclined to use general descriptors. However, compared with other common fruits, pears have more abundant texture types, so it is necessary to establish a texture evaluation system for pears. In this study, 156 germplasm resources of crisp pear germplasm were taken as the research object. The correlation between fruit puncture determination parameters was analyzed, and the varieties were classified based on sensory evaluation and texture determination. Then, the comprehensive score formula of principal components of was established through principal component analysis of puncture determination parameters, and the factors affecting the texture of pear flesh were analyzed so as to provide a theoretical basis for the establishment of accurate identification standards for the importance traits of crisp pear germplasm resources and the selection and utilization of resources.

2. Materials and Methods

2.1. Plant Material

In the National Germplasm Repository of Pear and Apple (xingcheng, China), 156 crisp pear fruit germplasm resources of Asian pear were selected from several systems, covering all the crisp-flesh types of P. bretschneideri, P. pyrifolia, P. sinkiangensis, and P. ussuriensis pears. For each variety, ≥20 fruits were harvested and stored at 4 °C. In addition, six representative crisp pear varieties, namely ‘Korla pear’ (P. sinkiangensis), ‘Shuihong Xiao’ (P. bretschneideri), ‘Xuehua’ (P. bretschneideri), ‘Qiubai’ (P. bretschneideri), ‘Baozhu’ (P. pyrifolia), and ‘Chili’ (P. bretschneideri), were picked for the determination of shelf life puncture parameters. The number of pears picked for each variety was 200. They were stored at 4 °C. Harvest timing for all 156 varieties followed the maturity standards documented in Chinese Pear Genetic Resources, [11], with exact harvest dates recorded.

2.2. Puncture Test and Parameter Setting

The Texture Measurement System–Professional Food Texture Analyzer (Food Technology Corporation, Sterling, VA, USA) was used to assess the fruits with no surface damage and no pests and diseases. Each fruit was punctured once from the front and back, ensuring that the puncture position was located in the central area of the fruit. When fruits that have reached harvestable maturity are stored at room temperature for a week after harvest, the flesh is fully ripe and has the best flavor and texture [36,37]. Accordingly, after cold storage at 4 °C, pear samples were transferred to room-temperature (25 °C) conditions. Texture measurements were conducted on day 7, using 10 fruits per variety. For shelf life puncture tests, 200 fruits per variety were maintained in a product observation chamber (25 °C). The first puncture tests were performed immediately after removal from cold storage (day 0), followed by subsequent measurements every 4 days using 20 randomly selected fruits per time point, and the results were averaged.
The puncture test was conducted to determine the texture of pear flesh and followed an existing pear flesh puncture test [27]; the parameter settings were slightly modified. Before the test, the pear fruit was peeled on both sides of the equator and placed into the puncture fixation device of the texture analyzer. The diameter of the probe was 6 mm, and the parameters were set as follows: trigger force 2 kg·m/s2 (N), test speed 60 mm·min−1, puncture distance 10 mm, and return speed 100 mm·min−1 (Figure 1). The measurement parameters and definitions are shown in Table 1. The fruit diameter was automatically output by the texture analyzer.

2.3. Sensory Evaluation of Flesh Texture

For each variety, 10 fruits with no surface damage and no pests and diseases were peeled, cut into evenly sized pieces, and mixed together for sensory texture evaluation. Sensory texture evaluation was carried out by experts trained in pear germplasm resources who compiled 10 classifications of flesh texture types in the book Chinese Pear Genetic Resources [11], which covers all texture categories, from melting (low hardness, high viscosity) to dense (high hardness, low porosity). The crispy flesh texture descriptors and their quantitative assignment are as follows: 6. loose; 7. crunchy; 8. crisp; 9. tight and crisp; 10. dense.

2.4. Statistical Analysis

Microsoft Excel 2016 (https://www.microsoft.com/en-us/microsoft-365/excel, accessed on 17 March 2025) was used for data sorting, SPSS27.0 (https://www.ibm.com/products/spss-statistics, accessed on 17 March 2025) software was used for correlation analysis, principal component analysis, and cluster analysis, and Origin 2022 (https://www.originlab.com, accessed on 17 March 2025) software was used for plotting.

3. Results

3.1. Variation and Correlation Analysis of Puncture Measurement Parameters

The averages, extreme values (Min and Max), standard deviations, and coefficients of variation (CV) of six puncture measurement parameters were analyzed (Table 2). WFLC (N·mm) had the highest coefficient of variation (0.281), with a range of 11.40~53.63 N·mm, followed by FLC (N), FF (N), S (N·mm−1), and W10 (N·mm). The coefficient of variation was 0.242, 0.232, 0.226, and 0.219, and the range of variation was 10.71~42.41 N, 11.88~38.59 N, 5.29~21.81 N·mm−1, and 96.11~290.85 N·mm. The coefficient of variation for D (mm) was the smallest (0.136), and the range of variation was 1.24~2.45 mm. Most penetration parameters exhibited coefficients of variation exceeding 15%, demonstrating significant varietal differences in puncture characteristics. The parameters of the puncture test could be used to distinguish the texture characteristics of varieties.
Correlation analysis showed that there was a significant correlation among all parameters (Figure 2). FF (N) and W10 (N·mm) had the most significant correlation, and the correlation coefficient was the largest (0.992). FLC (N) was positively correlated with W10 (N·mm), FF (N), and WFLC (N·mm) (p < 0.01), and the correlation coefficients were 0.921, 0.92, and 0.865, respectively. Puncture parameters directly related to force had a high positive correlation. However, D (mm) and S (N·mm−1) showed an extremely significant negative correlation (p < 0.01), and the correlation coefficient was −0.213. D (mm) was the deformation of flesh with breaking force, while S (N·mm−1) was the gradient measurement of puncture hardness, and the two indicators showed a negative correlation, which may be related to the greater variation in FLC and S.

3.2. Cluster Analysis Based on Sensory Texture Evaluation and Puncture Measurement

To investigate the associations between sensory texture evaluations and instrumental measurements, we performed correlation analyses between these parameters. The analysis revealed highly significant positive correlations between sensory texture scores and all six puncture measurement parameters. The strongest correlation (Table 3) occurred between sensory scores and FF (N), demonstrating that hardness represents the most influential texture parameter in sensory evaluation. According to the sensory texture score and puncture measurement parameters, systematic clustering and the gradual aggregation method were used to perform cluster analysis, and the approximate matrix was obtained by square Euclidean distance. At a clustering distance threshold of 5, the 156 crisp pear germplasm accessions were segregated into five distinct clusters (A-E). Cluster A contained 73 varieties, followed by cluster B, which contained 29 varieties, cluster C, which contained 24 varieties, cluster D, which contained 20 varieties, and cluster E, which contained 10 varieties (Figure 3). The five clusters showed strong correspondence with the five recognized crisp pear texture types in standard pear resource classifications.
The six puncture measurement parameters of cluster E resources were higher than those of the other four types of resources (Figure 4a). The mean values of FLC (N), D (mm), WFLC (N·mm), FF (N), W10 (N·mm), and S (N·mm−1) were 38.93 N, 2.14 mm, 45.74 N·mm, 34.28 N, 268.68 N·mm, and 18.57 N·mm−1, respectively (Table 4), and the sensory texture scores of the 10 varieties of cluster E resources were all 10 (Figure 4b). Cluster E resources belong to dense flesh type. The six puncture measurement parameters in cluster D ranked second among the five clusters of resources, second only to cluster E resources (Figure 4a). The mean values of FLC (N), D (mm), WFLC (N·mm), FF (N), W10 (N·mm), and S (N·mm−1) were 32.63 N, 2.04 mm, 37.86 N·mm, 28.76 N, 228.73 N·mm, and 16.42 N·mm−1, respectively (Table 4). The varieties with sensory texture scores of 9 were mostly in cluster D resources (Figure 4b). Cluster D resources belong to the tight-and-crisp flesh type. The six parameters of cluster C resources are significantly lower than those of the other four types of resources (Figure 4a). The mean values of FLC (N), D (mm), WFLC (N·mm), FF (N), W10 (N·mm), and S (N·mm−1) are 16.71 N, 1.67 mm, 17.93 N·mm, 15.40 N, 128.05 N·mm, and 10.35 N·mm−1, respectively (Table 4). Most of the varieties with a sensory texture score of 6 were in cluster C resources (Figure 4b), and cluster C resources belonged to the loose flesh type. The two resource indicators of cluster A and cluster B are located in the middle level (Figure 4a). The mean values of FLC (N), D (mm), WFLC (N·mm), FF (N), W10 (N·mm), and S (N·mm−1) of cluster A resources are 26.75 N, 1.70 mm, 26.74 N·mm, 23.26 N, 192.50 N·mm, and 16.03 N·mm−1, respectively. The mean values of FLC (N), D (mm), WFLC (N·mm), FF (N), W10 (N·mm), and S (N·mm−1) for cluster B resources are 23.58 N, 2.04 mm, 29.23 N·mm, 20.96 N, 166.63 N·mm, and 11.63 N·mm−1, respectively (Table 4). The sensory texture scores were mostly 7 and 8 (Figure 4b), belonging to the crunchy and crisp-flesh types, and these two descriptions are difficult to distinguish by sensory evaluation. Overall, the above results indicate that there is good agreement between sensory evaluation and texture instrument puncture determination, both of which divide the variety resources of crisp pear into five germplasm types, and flesh hardness is the main factor affecting texture evaluation. These findings provide valuable scientific support for establishing standardized protocols for crisp pear texture assessment and varietal classification.

3.3. Principal Component Analysis of Puncture Measurement Parameters

According to the analysis results (Table 5), the parameters are mainly divided into two principal components. The characteristic values are 4.475 and 1.360. The weights of FF (N), FLC (N), W10 (N·mm), and WFLC (N·mm) for the first principal component are relatively large; FF (N) has the largest eigenvalue, followed by FLC (N). The pairings of the four parameters are all higher than 0.8, showing a significant positive correlation (Figure 2). Moreover, these four measurement parameters represent the elasticity and hardness of flesh. They reflect the masticatory characteristics of flesh. The second principal component S (N·mm−1) has the largest negative weight, and D (mm) has the largest positive weight. These two parameters have an extremely significant negative correlation, which indicates that the degree of fragmentation and fracture of flesh sections reflects the cracking characteristics of flesh. The variance contribution rates of the first and second principal components are 74.591% and 22.672%, respectively, and the cumulative variance contribution rates are 74.591% and 97.262%, respectively. And the cumulative variance contribution rate of these two principal components was 97.262%, which basically reflected the flesh elasticity, hardness, and crispness of the 156 crisp pear germplasm resources and was highly representative of the texture and quality information of crisp pear.
Principal component analysis was performed to calculate component score coefficients (Table 4), yielding the following two principal component expressions:
Y1 = 0.464 × ZFF + 0.459 × ZFLC + 0.459 × ZW10 + 0.436 × ZWFLC + 0.348 × ZS + 0.228 × ZD;
Y2 = −0.0129 × ZFF − 0.1055 × ZFLC − 0.1149 × ZW10 + 0.3078 × ZWFLC − 0.5677 × ZS + 0.7477 × ZD.
ZFF, ZFLC, ZW10, ZWFLC, ZS and ZD represent values normalized by standard deviation (Z-score) for FF (N), FLC (N), W10 (N·mm), WFLC (N·mm), S (N·mm−1), and D (mm), respectively. The comprehensive model of principal component analysis was Y = 0.74591 × Y1 + 0.22672 × Y2.
The Z-score values of the texture parameters of 156 crisp pear germplasm resources were substituted into the two formulas Y1 and Y2, respectively, to obtain the scores of principal component 1 and principal component 2 of each variety. Then, the comprehensive score of principal component analysis of each variety was calculated by the comprehensive model of principal component analysis, which could also be regarded as the texture score of puncture determination (Figure 5). The results were sorted according to the comprehensive score of principal component analysis, and the texture scores measured by puncture were compared with those evaluated by sensory texture (Table S1). For example, ‘Naixibute’, ‘Tiepi’, and ‘Xingyi Haizili’ were the top 3 varieties among the 156 varieties, with overall texture scores of 4.66, 3.86, and 3.70, respectively. Their sensory texture scores were also the highest, at 10 for all of these, and the texture type of the flesh was dense. ‘Ganlizao 8’, ‘Mapiao’, and ‘Anhui Xueli’ were the three varieties with the lowest overall texture scores, which were −3.13, −3.15 and −3.24, respectively. Their sensory texture scores were also relatively low, at 6 or 7.
Among the 36 varieties with texture scores greater than 1, all the varieties with cluster E and cluster D resources were included, and the texture parameters of cluster E and cluster D resources were higher than those of cluster A, cluster B, and cluster C resources. The varieties belonging to cluster E with dense-type flesh include ‘Naixibute’, ‘Tiepi’, and ‘Xingyi Haizili’, and so on. The varieties belonging to cluster D with tight- and crisp-type flesh include ‘Xingcheng Xiehuatian’, ‘Huangmacha’, ‘Youhongxiao’, etc. The range with scores greater than −1.36 and less than 1 has 90 varieties, including most of the intermediate cluster A resources and all cluster B resources. There are 30 varieties with scores less than −1.36, including all cluster C resources, such as ‘Dangshan suli’, ‘Yuluxiang’, ‘Cuiguan’, etc. The sensory texture scores and instrumental texture scores exhibited consistent trends, where higher values corresponded to more compact tissue structures, while lower values indicated looser textures. This agreement demonstrates that both evaluation methods effectively capture textural differences among crisp pear germplasm resources.

3.4. Analysis of Influencing Factors of Fruit Texture

Maturity and fruit size represent crucial agronomic traits primarily governed by genetic background. The analysis of their correlation with flesh texture is of great significance for screening important genetic resources and carrying out directional breeding for important traits. Correlation analysis (Table 6) showed that fruit maturity was significantly positively correlated with sensory texture score, and the correlation coefficient was 0.200. Fruit maturity was significantly positively correlated with FLC (N), WFLC (N·mm), FF (N), and W10 (N·mm), and the correlation coefficients were 0.200, 0.166, 0.198, and 0.201; that is, maturity was strongly correlated with all the puncture parameters of the first principal component.
The correlation between pear fruit diameter and fruit weight reached 0.948. It can be used as the main index to evaluate pear fruit size [32]. We analyzed the correlation between pear fruit diameter and texture (Table 6). The results showed that there was no correlation between fruit diameter, sensory texture score, and puncture measurement parameters. Previous studies typically controlled experimental variables by selecting uniformly sized samples for texture analysis. Based on these results, we hypothesized that when other texture-affecting variables remain constant, fruit diameter may not be a primary determinant of textural characteristics.
Based on harvest timing, pear varieties can be classified into three maturation groups: early maturity (July to mid-August), medium maturity (mid-August to mid-September), and late maturity (mid-September through October) [38,39]. The crisp pear germplasm resources in this study were divided into three categories—early, middle, and late—at a proportion of 1:48:177, and most of the tested crisp pears matured after September (Figure 6a). Late-maturity varieties accounted for a relatively high proportion of each category of resources, among which 90% of cluster E resources belonged to late-maturity varieties. Fifteen varieties with sensory scores of 10 belonged to late-maturity varieties; among them, fourteen belonged to late-maturity varieties, and the cluster with a tight flesh texture was also late-maturity (Figure 6b). Appropriate harvesting maturity has an important impact on fruit texture quality. Fully matured fruits have a sufficient accumulation of various nutrients in their bodies and have better quality and storage endurance [39,40]. Therefore, sensory texture evaluation and instrumental measurement data will be closer to each other. This property can be used as an important reference for texture character screening.
Since fruit texture undergoes dynamic changes during postharvest ripening, investigating textural changes in crisp pears during shelf life is critical for establishing reliable quality evaluation criteria and guiding pear variety selection and utilization. For the six representative crisp pear varieties, the puncture parameters were measured every 4 days after harvest (Figure 7). The six puncture parameters of ‘Qiubaili’ had the highest level among the six crisp pear varieties. In particular, the values of the three parameters of FLC (N), FF (N), and W10 (N·mm) were much higher than those of the other five varieties in the whole shelf period, and four parameters of ‘Qiubaili’—FLC (N), FF (N), W10 (N·mm), and S (N mm−1)—all reached the highest values on the fourth day of shelf life and then declined. The values of the six puncture parameters of ‘Shuihongxiao’ and ‘Chili’ were the lowest among the six crisp pear varieties. The values of FLC (N), WFLC (N·mm), and D (mm) for ‘Xuehua’, related to rupture force, reached the highest level when the shelf life was 20 days. In contrast, the values of FF (N), W10 (N·mm), and S (N mm−1), three hardness-related indicators, were in decline since the start of shelf life. The overall trend of five parameters of ‘Korla pear’, including FLC (N), WFLC (N·mm), FF (N), W10 (N·mm), and S (N mm−1), was a decreasing one, but there was a fluctuation in the eighth day of shelf life, followed by a decline. On the whole, with the extension of shelf life, D (mm) and WFLC (N·mm) showed an upward trend, S (N mm−1) decreased significantly, FF (N) and W10 (N. mm) showed a slight downward trend, and FLC (N) showed a relatively gentle trend (Figure 7). This phenomenon may be attributed to concurrent changes in fruit water loss and cell wall modifications during shelf life, which collectively alter textural properties, including hardness and brittleness, consequently affecting puncture measurement parameters.

4. Discussion

The puncture test serves as an instrumental simulation of the mastication process, and the textural parameters obtained through this method provide reliable quantitative indicators that closely correlate with human sensory perception during fruit consumption. In this study, the variation coefficient of most of the puncture parameters is large, which proves that the texture diversity of pear varieties is rich. These findings validate the effectiveness of puncture measurements as a reliable method for assessing and characterizing textural properties in crisp pears. Among them, the work associated with the flesh limit compression force has the largest coefficient of variation, reflecting the work required for the flesh to break, as well as the magnitude of the rupture force and the displacement of the rupture force. The flesh limit compression force, to some extent, represents the crispness of the fruit [41]. Flesh firmness and the flesh work required to attain a flesh deformation of 10 mm of pear fruit, as well as the rupture force and rupture function, were significantly and positively correlated, which was consistent with the results of puncture tests on apples [42]. The correlation analysis of puncture measurement parameters in this experiment showed significant correlations among parameters representing flesh elasticity and hardness. FF (N), FLC (N), W10 (N·mm), and WFLC (N·mm), which reflect flesh mastication characteristics, were significantly positively correlated. This is consistent with the results of previous studies on pears [7,14,41], apples [24,43,44], and grapes [45]. These indicators can be used to comprehensively evaluate the flesh quality properties of crispy pear fruits.
Cluster analysis is a statistical method to cluster research samples according to their inter-related properties. Because of the high correlation between the relevant indexes of texture traits, cluster analysis is usually carried out after the dimensionality of variables is reduced through principal component analysis to obtain the principal factor. Research on thin-skinned melon extracted three principal factors from eight texture parameters and six chemical indexes through principal component analysis [46]. Then, muskmelon was divided into five types formed by the main indexes obtained by systematic clustering. The assessment of flesh texture can be effectively conducted through both sensory evaluation and instrumental texture analysis. Their complementary application provides a comprehensive approach that combines subjective human perception with objective quantitative measurements, thereby yielding the most reliable and representative characterization of textural properties. Based on this, this experiment combined the two methods to cluster 156 crispy pear germplasm resources, and finally, the crisp pears were divided into five clusters, A, B, C, D, and E, corresponding to five germplasm types: loosen, crunchy, crisp, tight–crisp, and dense. The results showed that the current standard of sensory evaluation of crisp pear flesh texture was in good agreement with the results of texture instrument puncture tests. The tests could be used for the standardized evaluation of pear varieties. Chinese Pear Genetic Resources also includes fruit texture descriptors for soft pears in ten sensory texture categories (1. melting; 2. soft; 3. mealy; 4. fluffy; 5. sandy; 6. loose; 7. crunchy; 8. crisp; 9. tight and crisp; 10. dense), which can be used as a reference in subsequent studies to determine whether a texture evaluation system for all texture types of pear germplasm resources can be formed through this set of descriptors. In this study, a texture evaluation model was obtained through principal component analysis to describe the physical quality of crispy fruit. The puncture score model represents the value of fruit texture. The higher the puncture score, the denser the flesh texture. This is consistent with previous evaluation models representing texture quality in pear [47], fresh jujube [48,49], mulberry [50], and cucumber [34].
The textural variation among the five crisp pear germplasm clusters may be attributed to distinct physicochemical properties, with differential gene expression influencing key determinants including stone cell development, pectin metabolism, and cell wall architecture across cultivars. Using cluster analysis, we were able to classify the 14 cultivars of crisp pear into three different texture kinds, namely, soft (such as ‘Housui’, ‘Hwangkum’, ‘Huangguan’, etc.), crisp (‘such as Mansoo’), and hard (‘Butirra Rostata Morettini’) [7]. Stone cells are composed of cellulose, hemicellulose, and lignin, with high physical strength, and their quantity and distribution directly affect the hardness and porosity of flesh [51,52]. The results demonstrated a significant positive correlation between stone cell content and flesh hardness, indicating that higher stone cell levels correspond to greater flesh firmness [10]. A key transcription factor (PbAGL7) which promotes stone cell content and secondary cell wall thickening was identified through transcriptome analysis of pear varieties with varying stone cell levels. Additionally, sclerenchyma cell abundance directly influences pear crispness [53]. PbrMYB4, an R2R3-MYB protein, regulates the lignification of pyritic cells by activating lignin synthesis genes. Overexpression of PbrMYB4 enhances lignin deposition and increases the cell wall thickness of ducts and wood fibers [54]. The decomposition of cellulose, hemicellulose, and pectin in the cell wall is primarily mediated by polygalacturonase (PG), pectin methylesterase (PME), pectatc lyase (PL), rhamnogalacturonase (RGase), cellulase (Cx), and β-galactosidase (TBG) [55]. These factors likely underlie the distinct textural variations observed among the five clusters of germplasm resources examined in this study. For future research on flesh texture-associated genes, these characterized resources provide a valuable foundation, enabling precise identification and evaluation of textural traits in pear cultivars.
After principal component analysis, it was found that there were overlapping distributions between sensory texture scores and puncture parameters in the principal component biplot when grouped by sensory texture scores. Using sensory texture evaluation as the dependent variable and individual texture parameters as independent variables, the authors developed a multiple linear regression model (R2 = 0.564). This indicates that the six puncture measurement parameters could explain only 56.4% of the variation in sensory texture evaluation, a finding consistent with previous studies on apple sensory evaluation models [18,56]. Texture measurement parameters can only explain half or less of sensory juiciness and peel toughness, suggesting these textural attributes are influenced by multiple complex factors. Although sensory texture evaluation has a significant correlation with texture data, the sensory texture score is not always identical with puncture measurement data. These six puncture measurements cannot fully account for all the texture changes. Pear flesh exhibits diverse textural properties, with significant varietal differences observed among cultivars [10]. Sensory evaluation alone presents limitations for quantitative texture assessment due to the inherent subjectivity in human perception. Flesh texture variation is associated with multiple physicochemical components, including pectin [9,43], cellulose [57], lignin [58,59], and starch [60,61]. Additional texture-related components should be incorporated into future sensory texture modeling studies.
This study demonstrated that maturity significantly influences pome fruit flesh quality, while fruit size appears less critical. We hypothesize that late-ripening varieties, benefiting from extended development periods, achieve greater dry matter accumulation [62] and higher concentrations of texture-related compounds such as lignin [63]. However, regional climatic differences, particularly between northern and southern growing areas, lead to variations in pear ripening periods. Under field conditions, the maturity for a given variety is defined as the date when 70% of fruits reach full maturity [38]. Fruit maturity was additionally assessed through peel color transition, specifically from green to yellow-green or green to brown. However, the current understanding of varietal and regional differences in fruit maturity characteristics remains limited [39]. After-ripening is usually an external factor affecting fruit quality. The texture changes of crisp pear germplasm resources during shelf life showed that D (mm) and WFLC (N·mm) had an increasing trend, S (N·mm−1) decreased significantly, FF (N) and W10 (N·mm) showed a slight decreasing trend, and FLC (N) showed a gentle trend. Optimal harvest maturity significantly influences both fruit quality and storage potential. Pears harvested at different maturity stages exhibit distinct changes in flesh texture parameters during room-temperature shelf life [7]. When harvested at optimal maturity, ripening progression shows significant correlations with both instrumental puncture measurements and sensory texture evaluations. Shelf life varied considerably among pear varieties depending on their maturation period. Early-season varieties (late July to early August) exhibited shorter shelf lives (<10 days), while late-season varieties (early September onward) demonstrated superior storage potential. Among the six tested varieties, late-maturing types showed an extended shelf life of up to 28 days, with FLC (N) displaying relatively stable trends, a characteristic potentially associated with enhanced storage capacity in late-maturing cultivars. These results significantly contribute to texture evaluation in crisp pears, enable an effective selection of premium genetic resources, and support precision breeding approaches to streamline cultivar improvement.

5. Conclusions

Using puncture tests and sensory evaluation, we assessed texture characteristics in 156 crisp pear germplasm resources. The results demonstrated a substantial diversity in texture traits. Instrumental measurements and sensory evaluations showed strong consistency, with cluster analysis dividing the pears into five clusters: loose, crunchy, crisp, tight–crisp, and dense. Principal component analysis of the six puncture measurement parameters resulted in two principal components reflecting the flesh mastication characteristics and the flesh cracking characteristics. The principal component-derived texture scores closely matched sensory evaluation trends, confirming the effectiveness of this combined approach for texture assessment. Further analysis of the influencing factors of flesh texture showed that fruit maturity and shelf life had significant effects on flesh quality, but fruit diameter did not seem to be the main factor. This study provides an important reference for the standardization, evaluation, and utilization of crisp pear variety resources.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/horticulturae11040359/s1, Table S1: Comprehensive score table of fruit texture of 156 crisp-flesh pear germplasm resources; Table S2: Fruit maturity, fruit diameter, puncture data, and cluster group statistics of crisp-flesh pear varieties.

Author Contributions

Y.M. and X.D., writing—original draft, visualization, data curation, software; N.L., C.Y. and X.Y., formal analysis, software; L.T., Y.Z., H.H., D.Q., J.X. and C.L., resources; Y.M. and X.D., conceptualization, writing—review and editing; X.D. supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by The Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP-RIP), the Earmarked Fund for the China Agriculture Research System (CARS-28-01), and National Natural Science Foundation of China project (32272676).

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Cao, Y.F.; Li, S.L.; Huang, L.S.; Sun, J.Z.; Tan, X.W. Survey of germplasm resources of pear in China and comprehensive evaluation of excellent germplasm. China Fruits 2000, 4, 42–44. [Google Scholar] [CrossRef]
  2. Wang, W.H.; Wang, G.P.; Tian, L.M.; Li, X.G.; Lv, X.L.; Zhang, Y.X.; Zhang, J.H.; Cao, Y.F. New China fruit science research 70 years—Pear. J. Fruit Sci. 2019, 36, 1273–1282. [Google Scholar] [CrossRef]
  3. Harker, F.R.; Redgwell, R.J.; Hallett, I.C.; Murray, S.H.; Carter, G. Texture of Fresh Fruit. In Horticultural Reviews; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 1997; pp. 121–224. ISBN 978-0-470-65064-6. [Google Scholar]
  4. Cano-Salazar, J.; López, M.L.; Echeverría, G. Relationships between the instrumental and sensory characteristics of four peach and nectarine cultivars stored under air and CA atmospheres. Postharvest Biol. Technol. 2013, 75, 58–67. [Google Scholar] [CrossRef]
  5. Jaeger, S.R.; Andani, Z.; Wakeling, I.N.; MacFie, H.J.H. Consumer preferences for fresh and aged apples: A cross-cultural comparison. Food Qual. Prefer. 1998, 9, 355–366. [Google Scholar] [CrossRef]
  6. Corrigan, V.K.; Hurst, P.L.; Boulton, G. Sensory characteristics and consumer acceptability of ‘Pink Lady’ and other late-season apple cultivars. N. Z. J. Crop Hortic. Sci. 1997, 25, 375–383. [Google Scholar] [CrossRef]
  7. Wang, Y.X.; Wang, X.M.; Guan, J.F. Flesh Texture Characteristic Analysis of Pear. Agric. Sci. China 2014, 47, 4056–4066. [Google Scholar] [CrossRef]
  8. Atkinson, R.G.; Gunaseelan, K.; Wang, M.Y.; Luo, L.; Wang, T.; Norling, C.L.; Johnston, S.L.; Maddumage, R.; Schröder, R.; Schaffer, R.J. Dissecting the role of climacteric ethylene in kiwifruit (Actinidia chinensis) ripening using a 1-aminocyclopropane-1-carboxylic acid oxidase knockdown line. J. Exp. Bot. 2011, 62, 3821–3835. [Google Scholar] [CrossRef]
  9. Lunn, J.E.; MacRae, E. New complexities in the synthesis of sucrose. Curr. Opin. Plant Biol. 2003, 6, 208–214. [Google Scholar] [CrossRef]
  10. Dong, X.G.; Tian, L.M.; Cao, Y.F.; Zhang, Y.; Qi, D. Factor analysis and comprehensive evaluation of fruit quality in cultivars of Pyrus pyrifolia (Burm. f.) Nakai from south China. J. Fruit Sci. 2014, 31, 815–822. [Google Scholar] [CrossRef]
  11. Cao, Y.F.; Zhang, S.L. Chinese Pear Genetic Resources; China Agriculture Press: Beijing, China, 2020; ISBN 978-7-109-26808-1. [Google Scholar]
  12. Harker, F.R.; Marsh, K.B.; Young, H.; Murray, S.H.; Gunson, F.A.; Walker, S.B. Sensory interpretation of instrumental measurements 2: Sweet and acid taste of apple fruit. Postharvest Biol. Technol. 2002, 24, 241–250. [Google Scholar] [CrossRef]
  13. Infante, R.; Meneses, C.; Byrne, D.H. Present Situation of Peach Breeding Programs: Post Harvest and Fruit Quality Assessment. Acta Hortic. 2006, 713, 121–124. [Google Scholar] [CrossRef]
  14. Lozano, L.; Iglesias, I.; Puy, J.; Echeverria, G. Performance of an Expert Sensory Panel and Instrumental Measures for Assessing Eating Fruit Quality Attributes in a Pear Breeding Programme. Foods 2023, 12, 1426. [Google Scholar] [CrossRef] [PubMed]
  15. Hampson, C.R.; Quamme, H.A.; Hall, J.W.; MacDonald, R.A.; King, M.C.; Cliff, M.A. Sensory evaluation as a selection tool in apple breeding. Euphytica 2000, 111, 79–90. [Google Scholar] [CrossRef]
  16. Mitchell, J. Food Texture and Viscosity: Concept and Measurement. Int. J. Food Sci. Technol. 2003, 38, 839–840. [Google Scholar] [CrossRef]
  17. Ross, C.F. Sensory science at the human–machine interface. Trends Food Sci. Technol. 2009, 20, 63–72. [Google Scholar] [CrossRef]
  18. Bejaei, M.; Stanich, K.; Cliff, M.A. Modelling and Classification of Apple Textural Attributes Using Sensory, Instrumental and Compositional Analyses. Foods 2021, 10, 384. [Google Scholar] [CrossRef] [PubMed]
  19. Kilcast, D.; Fillion, L. Understanding consumer requirements for fruit and vegetable texture. Nutr. Food Sci. 2001, 31, 221–225. [Google Scholar] [CrossRef]
  20. Río Segade, S.; Orriols, I.; Giacosa, S.; Rolle, L. Instrumental Texture Analysis Parameters as Winegrapes Varietal Markers and Ripeness Predictors. Int. J. Food Prop. 2011, 14, 1318–1329. [Google Scholar] [CrossRef]
  21. Li, Y.H.; Chang, R.F.; Zhang, L.S.; Wang, Z.Y.; Chen, H.; Han, J.C.; Liu, G.J. The Optimization of Texture Determination of Fresh Peach by Using Texture Analyzer TPA. J. Hebei Agric. Sci. 2016, 20, 95–100. [Google Scholar] [CrossRef]
  22. He, G.Q.; Huang, M.H.; Zhang, E.Z.; Xin, M.; Huang, M.K.; Tan, R.Y.; Huang, Z.Y. Optimization for mango texture profile analysis and characterization of texture to different maturaity of mango. Sci. Technol. Food Ind. 2016, 37, 122–126. [Google Scholar] [CrossRef]
  23. Ma, Q.H.; Wang, G.X.; Liang, L.S. Establishment of the Detecting Method on the Fruit Texture of Dongzao by Puncture Test. Agric. Sci. China 2011, 44, 1210–1217. [Google Scholar] [CrossRef]
  24. Yang, L.; Xiang, L.; Wang, Q.; Zhang, C.X.; Cong, P.H.; Tian, Y. Study on texture properties of apple flesh by using texture profile analysis. J. Fruit Sci. 2014, 31, 977–985. [Google Scholar] [CrossRef]
  25. Li, J.K.; Lin, Y.; Zhang, P.; Qin, G.Z.; Li, B.Q.; Tian, S.P. Effect of 1-Methylcyclopropene Treatment at Different Times Postharvest on the Texture of Apple Fruits. Food Sci. 2013, 34, 277–281. [Google Scholar] [CrossRef]
  26. Wang, X.M.; Guan, J.F.; Wang, Y.X.; Liu, Y. Effect of 1-MCP on flesh texture of “Whangkeumbae” pear during ambient temperature storage. J. Hebei Agric. Univ. 2013, 36, 46–49. [Google Scholar] [CrossRef]
  27. Wang, F.; Jiang, S.L.; Chen, Q.J.; Ou, C.Q.; Zhang, W.J.; Hao, N.N.; Ma, L.; Li, L.W. Changes in fruit texture of crisp-flesh pear during fruit ripening. J. Fruit Sci. 2016, 33, 950–958. [Google Scholar] [CrossRef]
  28. Li, Y.H.; Zhang, L.S.; Chang, R.F.; Wang, S.Y.; Chen, H.; Liu, G.J. Change of Texture Properties of Three Peach Varieties During Postharvest Storage by Texture Profile Analysis. N. Hortic. 2016, 4, 133–137. [Google Scholar] [CrossRef]
  29. Yuan, C.L.; Dong, X.Y.; Li, P.H.; Li, D.L.; Duan, Y.X. Changes in Texture Properties of Crisp Peach during Postharvest Storage by Texture Profile Analysis. Food Sci. 2013, 34, 273–276. [Google Scholar] [CrossRef]
  30. Zhang, Y.; Liang, Y.L.; Pan, Q.W.; Zhang, W. Correlation between the Sensory Evaluation and Texture Profile Analysis of Kiwifruit. Sci. Technol. Food Ind. 2018, 39, 243–247+252. [Google Scholar] [CrossRef]
  31. Reng, Z.H.; Zhang, K.M.; Li, Z.W.; Nong, S.Z.; Zhang, P. Study on the evaluation of texture parameters of grape berry during storage by using texture profile analysis. Sci. Technol. Food Ind. 2011, 32, 375–378. [Google Scholar] [CrossRef]
  32. Zhang, Y.; Cao, Y.; Huo, H.; Xu, J.; Tian, L.; Dong, X.; Qi, D.; Liu, C. An assessment of the genetic diversity of pear (Pyrus L.) germplasm resources based on the fruit phenotypic traits. J. Integr. Agric. 2022, 21, 2275–2290. [Google Scholar] [CrossRef]
  33. Shen, S.Y.; Wang, Z.Q.; Zhang, Q.; Yang, J.; Han, F.; Zhong, C.H.; Wang, C.H.; Huang, W.J. Analysis of fruit quality and sensory evaluation of 36 kiwifruit (Actinidia) germplasm accessions. J. Integr. Plant Biol. 2023, 41, 540–551. [Google Scholar] [CrossRef]
  34. Yang, Y.H.; Song, X.F.; Zhao, Y.H.; Li, X.L.; Cui, H.N.; Jia, J.H.; Yan, L.Y. Evaluation of fruit texture traits of cucumber germplasm resources. Jiangsu Agric. Sci. 2023, 51, 145–152. [Google Scholar] [CrossRef]
  35. Tang, R. Comprehensive Evaluation and Genetic Trend Analysis of Skin Texture of 50 Watermelon Germplasm Resource. Master’s Thesis, Northeast Agricultural University, Harbin, China, 2024. [Google Scholar]
  36. Blanckenberg, A.; Muller, M.; Theron, K.I.; Crouch, E.M.; Steyn, W.J. Harvest maturity and ripeness differentially affects consumer preference of ‘Forelle’, ‘Packham’s Triumph’ and ‘Abate Fetel’ pears (Pyrus communis L.). Sci. Hortic. 2016, 207, 131–139. [Google Scholar] [CrossRef]
  37. Zhang, M.-Y.; Xue, C.; Xu, L.; Sun, H.; Qin, M.-F.; Zhang, S.; Wu, J. Distinct transcriptome profiles reveal gene expression patterns during fruit development and maturation in five main cultivated species of pear (Pyrus L.). Sci. Rep. 2016, 6, 28130. [Google Scholar] [CrossRef] [PubMed]
  38. Wan, C.Y.; Mi, L.; Guo, D.; Qiao, Y.S.; Huo, H.Z.; Chen, B.Y.; Li, J.F.; Chen, X.P. Preliminary screening of Pyrus pyrifolia Nakai combination with different mature periods based on fuzzy synthetic evaluation of fruit quality. J. Northwest AF Univ. (Nat. Sci. Ed.) 2018, 46, 99–107. [Google Scholar] [CrossRef]
  39. Lee, B.-R.; Cho, J.-H.; Wi, S.G.; Yang, U.; Jung, W.-J.; Lee, S.-H. The Sucrose-to-Hexose Ratio is a Significant Determinant for Fruit Maturity and is Modulated by Invertase and Sucrose Re-Synthesis During Fruit Development and Ripening in Asian Pear (Pyrus pyrifolia Nakai) Cultivars. Hortic. Sci. Technol. 2021, 39, 141–151. [Google Scholar] [CrossRef]
  40. Byun, J.; Kim, D.H.; Lee, D.; Kang, I.; Chang, K.; Shin, S.L. Changes of Pectic Substances and Polygalacturonase Activity during Fruit Development of Various Peach Cultivars with Degrees of Fruit Softening. J. Korean Soc. Hortic. Sci. 2003, 44, 503–507. [Google Scholar]
  41. Gao, H.S.; Jia, Y.R.; Wei, J.M.; Ran, X.T.; Le, W.Q. Studies on the Post-harvested Fruit Texture Changes of ‘Yali’ and ‘Jingbaili’ Pears by Using Texture Analyzer. Hortic. Plant J. 2012, 39, 1359–1364. [Google Scholar] [CrossRef]
  42. Brookfield, P.L.; Nicoll, S.; Gunson, F.A.; Harker, F.R.; Wohlers, M. Sensory evaluation by small postharvest teams and the relationship with instrumental measurements of apple texture. Postharvest Biol. Technol. 2011, 59, 179–186. [Google Scholar] [CrossRef]
  43. Gálvez-López, D.; Laurens, F.; Devaux, M.F.; Lahaye, M. Texture analysis in an apple progeny through instrumental, sensory and histological phenotyping. Euphytica 2012, 185, 171–183. [Google Scholar] [CrossRef]
  44. Du, X.M.; Zhao, Q.C.; Lv, K.; Liu, J.Y.; Cheng, S.F.; Ma, Y.S. Comparison of Texture Determination Method and Correlation Analysis with Sensory Evaluation of 5 Kinds of Apple. Sci. Technol. Food Ind. 2020, 41, 240–246. [Google Scholar] [CrossRef]
  45. Zhang, W.; Mayinur, J.M.L.; Wang, M.; Han, S.A.; Xie, H.; Pan, Q.M. Analysis on the Flesh Texture, Cell Architecture andIts Physiological Characteristics of Different Grape Varieties. Acta Bot. Boreali-Occident. Sin. 2022, 42, 1870–1879. [Google Scholar] [CrossRef]
  46. Pan, H.B.; Liu, D.; Shao, Q.X.; Gao, G.; Qi, H.Y. Analysis and Comprehensive Evaluation of Textural Quality of Ripe Fruits from Different Varieties of Oriental Melon (Cucumis melo var. makuwa Makino). Food Sci. 2019, 40, 35–42. [Google Scholar] [CrossRef]
  47. Xu, Y.Q.; Tian, L.M.; Cao, Y.F.; Dong, X.G.; Qi, D.; Huo, H.L. Evaluation and analysis of flesh texture of six pear varieties with different shelf life after cold storage. China Fruits 2024, 9, 14–23. [Google Scholar] [CrossRef]
  48. Wu, S.; Jia, Y.L.; Zhi, F.J.; Wei, W. Multivariate Statistical Analysis of 19 Characters of 31 Jujube Resources. J. Hebei Agric. Sci. 2020, 24, 56–62+70. [Google Scholar] [CrossRef]
  49. Yang, Z.; Wang, Z.L. Evaluation and Cluster Analysis of Jujube Fruit Texture Based on TPA Method. Xinjiang Acad. Agric. Sci. 2019, 56, 1860–1868. [Google Scholar] [CrossRef]
  50. Fan, Z.P. Analysis and Comprehensive Evaluation of Fruit Quality of Different Mulberry Varieties. Master’s Thesis, Hebei Agriculture University, Baoding, China, 2020. [Google Scholar]
  51. Yan, C.; Yin, M.; Zhang, N.; Jin, Q.; Fang, Z.; Lin, Y.; Cai, Y. Stone cell distribution and lignin structure in various pear varieties. Sci. Hortic. 2014, 174, 142–150. [Google Scholar] [CrossRef]
  52. Wang, Y.; Cao, X.H.; Nian, R.; You, K.Y.; Zhu, D.S. Research Progress on Factors Controlling the Formation of Stone Cells in Pear Fruits and Their Effects on Fruit Texture. Food Sci. 2024, 45, 340. [Google Scholar] [CrossRef]
  53. Gong, X.; Qi, K.; Zhao, L.; Xie, Z.; Pan, J.; Yan, X.; Shiratake, K.; Zhang, S.; Tao, S. PbAGL7–PbNAC47–PbMYB73 complex coordinately regulates PbC3H1 and PbHCT17 to promote the lignin biosynthesis in stone cells of pear fruit. Plant J. 2024, 120, 1933–1953. [Google Scholar] [CrossRef]
  54. Liu, D.; Xue, Y.; Wang, R.; Song, B.; Xue, C.; Shan, Y.; Xue, Z.; Wu, J. PbrMYB4, a R2R3-MYB protein, regulates pear stone cell lignification through activation of lignin biosynthesis genes. Hortic. Plant J. 2025, 11, 105–122. [Google Scholar] [CrossRef]
  55. Payasi, A.; Mishra, N.N.; Chaves, A.L.S.; Singh, R. Biochemistry of fruit softening: An overview. Physiol. Mol. Biol. Plants Int. J. Funct. Plant Biol. 2009, 15, 103–113. [Google Scholar] [CrossRef]
  56. Cliff, M.A.; Bejaei, M. Inter-correlation of apple firmness determinations and development of cross-validated regression models for prediction of sensory attributes from instrumental and compositional analyses. Food Res. Int. 2018, 106, 752–762. [Google Scholar] [CrossRef] [PubMed]
  57. Hiwasa, K. European, Chinese and Japanese pear fruits exhibit differential softening characteristics during ripening. J. Exp. Bot. 2004, 55, 2281–2290. [Google Scholar] [CrossRef] [PubMed]
  58. Asrey, R.; Patel, V.B.; Singh, S.K.; Sagar, V.R. Factors affecting fruit maturity and maturity standards—A review. J. Food Sci. Technol. 2008, 45, 381–390. [Google Scholar]
  59. Prasad, K.; Jacob, S.; Siddiqui, M.W. Chapter 2—Fruit Maturity, Harvesting, and Quality Standards. In Preharvest Modulation of Postharvest Fruit and Vegetable Quality; Siddiqui, M.W., Ed.; Academic Press: Cambridge, MA, USA, 2018; pp. 41–69. ISBN 978-0-12-809807-3. [Google Scholar]
  60. Lin, H.T.; Xi, Y.F.; Chen, S.J. Postharvest Softening Physiological Mechanism of Huang hua Pear Fruit. Agric. Sci. China 2003, 36, 349–352. [Google Scholar]
  61. Shiga, T.M.; Soares, C.A.; Nascimento, J.R.; Purgatto, E.; Lajolo, F.M.; Cordenunsi, B.R. Ripening-associated changes in the amounts of starch and non-starch polysaccharides and their contributions to fruit softening in three banana cultivars. J. Sci. Food Agric. 2011, 91, 1511–1516. [Google Scholar] [CrossRef] [PubMed]
  62. Lopez, G.; Behboudian, M.H.; Echeverria, G.; Girona, J.; Marsal, J. Instrumental and Sensory Evaluation of Fruit Quality for ‘Ryan’s Sun’ Peach Grown under Deficit Irrigation. HortTechnology 2011, 21, 712–719. [Google Scholar] [CrossRef]
  63. Li, X.; Xu, C.; Korban, S.S.; Chen, K. Regulatory Mechanisms of Textural Changes in Ripening Fruits. Crit. Rev. Plant Sci. 2010, 29, 222–243. [Google Scholar] [CrossRef]
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Figure 1. Load–displacement curve obtained by puncture test of crisp pear with texture analyzer.
Figure 1. Load–displacement curve obtained by puncture test of crisp pear with texture analyzer.
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Figure 2. Correlation of puncture test parameters of 156 crisp pear germplasm resources.
Figure 2. Correlation of puncture test parameters of 156 crisp pear germplasm resources.
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Figure 3. Circular heatmap of puncture test parameters and sensory texture score of 156 crispy pear germplasm resources. Note: The red varieties are labeled as cluster A; the blue varieties are labeled as cluster B; the green varieties are labeled as cluster C; the purple varieties are labeled as cluster D; the yellow varieties are labeled as cluster E; STS represents sensory texture score.
Figure 3. Circular heatmap of puncture test parameters and sensory texture score of 156 crispy pear germplasm resources. Note: The red varieties are labeled as cluster A; the blue varieties are labeled as cluster B; the green varieties are labeled as cluster C; the purple varieties are labeled as cluster D; the yellow varieties are labeled as cluster E; STS represents sensory texture score.
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Figure 4. (a) Histogram of statistical analysis of six puncture parameters in different subgroups; (b) violin charts with sensory texture scores in different subgroups.
Figure 4. (a) Histogram of statistical analysis of six puncture parameters in different subgroups; (b) violin charts with sensory texture scores in different subgroups.
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Figure 5. Principal component analysis biplot of puncture test parameters based on sensory texture score. Note: The sensory texture score was used as the grouping category.
Figure 5. Principal component analysis biplot of puncture test parameters based on sensory texture score. Note: The sensory texture score was used as the grouping category.
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Figure 6. (a) Fruit maturity distribution of 156 crisp pear germplasm resources; (b) percentage stacking histogram of the five subgroups at different maturities.
Figure 6. (a) Fruit maturity distribution of 156 crisp pear germplasm resources; (b) percentage stacking histogram of the five subgroups at different maturities.
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Figure 7. Changes in puncture parameters of 6 crispy pear varieties during shelf life.
Figure 7. Changes in puncture parameters of 6 crispy pear varieties during shelf life.
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Table 1. Definitions of flesh puncture test parameters.
Table 1. Definitions of flesh puncture test parameters.
ParameterCalculation MethodDescription
Flesh limit compression force (FLC, N)Maximum force through the fleshRepresents the ultimate elasticity of flesh
Deformation associated with flesh limit compression force (D, mm)Distance at which breaking force is displacedDeformation of flesh with breaking force
Slope of force deformation curve (S, N·mm−1)Gradient slope of force from 0 to FLCGradient measurement of puncture hardness
Work associated with FLC (WFLC, N·mm)Area under the curve from 0 to DThe work required for the flesh to break
Work required to attain a flesh deformation of 10 mm (W10, N·mm)Displacement from 0 to 10mm area under the curveThe work required when the flesh is deformed to 10 mm
Flesh firmness (FF, N)Mean of force required to shift from D to 10 mmThe average force required to penetrate the flesh to 10 mm
Note: The FLC and FF units were Newton (N), 1N = kg·m/s2. During the puncture test, the probe was penetrated at a speed of 60 mm·min−1 to record flesh limit compression force and flesh firmness. The unit of D is mm, and it represents the displacement of the probe from contact with the flesh to penetration into the flesh. The unit of S is N·mm−1, and it represents the force required per 1 mm for the probe to penetrate the flesh before reaching the FLC. The units of WFLC and W10 are N·mm, which is the unit of work, representing the product of the force felt by the probe and its displacement in the direction of motion.
Table 2. Differences in puncture test parameters of 156 crisp pear germplasm resources.
Table 2. Differences in puncture test parameters of 156 crisp pear germplasm resources.
IndicatorsFLC/ND/mmWFLC/N·mmFF/NW10/N·mmS/N·mm−1
Average26.151.8328.4923.03187.3014.55
Min10.711.2411.4011.8896.115.29
Max42.412.4553.6338.59290.8521.81
Standard deviation6.320.258.025.3541.053.29
CV/%24.2013.6028.123.221.922.6
Table 3. Correlation between sensory texture scores and puncture test parameters of 156 crisp pear germplasm resources.
Table 3. Correlation between sensory texture scores and puncture test parameters of 156 crisp pear germplasm resources.
FLC/ND/mmWFLC/N·mmFF/NW10/N·mmS/N·mm−1
Sensory texture score0.698 **0.294 **0.648 **0.708 **0.696 **0.529 **
Note: ** is extremely significant (1% significant level).
Table 4. Comparison of the mean values of six puncture test parameters between different subgroups.
Table 4. Comparison of the mean values of six puncture test parameters between different subgroups.
SubgroupsFLC (N) ± SDD (mm) ± SDWFLC (N·mm) ± SDFF (N) ± SDW10 (N·mm) ± SDS (N·mm−1) ± SD
A26.75 ± 0.41.70 ± 0.0226.74 ± 0.5423.26 ± 0.37192.50 ± 2.916.03 ± 2.9
B23.58 ± 0.452.04 ± 0.0229.23 ± 0.4920.96 ± 0.3166.63 ± 2.3511.63 ± 2.35
C16.71 ± 0.471.67 ± 0.0517.93 ± 0.6515.40 ± 0.33128.05 ± 2.7310.35 ± 2.73
D32.63 ± 0.692.04 ± 0.0437.86 ± 0.7928.76 ± 0.5228.73 ± 4.3416.42 ± 4.34
E38.93 ± 0.732.14 ± 0.0545.74 ± 1.1634.28 ± 0.71268.68 ± 4.7518.57 ± 4.75
Table 5. Principal component analysis of puncture test parameters of 156 crisp pear germplasm resources.
Table 5. Principal component analysis of puncture test parameters of 156 crisp pear germplasm resources.
ParameterFeature Vector
First Principal
Component		Second Principal
Component
FF (N)0.982−0.015
FLC (N)0.971−0.123
W10 (N·mm)0.970−0.134
WFLC (N·mm)0.9230.359
S (N·mm−1)0.737−0.662
D (mm)0.4820.872
Characteristic value4.4751.360
Contribution rate/%74.59122.672
Cumulative contribution rate/%74.59197.262
Note: The extraction method is principal component analysis, and 2 components have been extracted.
Table 6. Correlation between maturity, fruit diameter, and puncture test parameters of 156 crispy pear germplasm resources.
Table 6. Correlation between maturity, fruit diameter, and puncture test parameters of 156 crispy pear germplasm resources.
Sensory Texture ScoreFLC/ND/mmWFLC/N·mmFF/NW10/N·mmS/N·mm−1
Maturity0.200 *0.200 *0.1200.166 *0.198 *0.201 *0.151
Fruit diameter0.0790.031−0.127−0.042−0.133−0.1260.099
Note: * is significantly correlated (5% significant level).
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Mou, Y.; Dong, X.; Zhang, Y.; Tian, L.; Huo, H.; Qi, D.; Xu, J.; Liu, C.; Li, N.; Yin, C.; et al. Identification and Evaluation of Flesh Texture of Crisp Pear Fruit Based on Penetration Test Using Texture Analyzer. Horticulturae 2025, 11, 359. https://doi.org/10.3390/horticulturae11040359

AMA Style

Mou Y, Dong X, Zhang Y, Tian L, Huo H, Qi D, Xu J, Liu C, Li N, Yin C, et al. Identification and Evaluation of Flesh Texture of Crisp Pear Fruit Based on Penetration Test Using Texture Analyzer. Horticulturae. 2025; 11(4):359. https://doi.org/10.3390/horticulturae11040359

Chicago/Turabian Style

Mou, Yulu, Xingguang Dong, Ying Zhang, Luming Tian, Hongliang Huo, Dan Qi, Jiayu Xu, Chao Liu, Niman Li, Chen Yin, and et al. 2025. "Identification and Evaluation of Flesh Texture of Crisp Pear Fruit Based on Penetration Test Using Texture Analyzer" Horticulturae 11, no. 4: 359. https://doi.org/10.3390/horticulturae11040359

APA Style

Mou, Y., Dong, X., Zhang, Y., Tian, L., Huo, H., Qi, D., Xu, J., Liu, C., Li, N., Yin, C., & Yang, X. (2025). Identification and Evaluation of Flesh Texture of Crisp Pear Fruit Based on Penetration Test Using Texture Analyzer. Horticulturae, 11(4), 359. https://doi.org/10.3390/horticulturae11040359

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