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Article

Assessment of the Addition of Cricket (Acheta domesticus) Powder to Chickpea (Cicer arietinum) and Flaxseed (Linum usitatissimum) Flours: A Chemometric Evaluation of Their Pasting Properties

by
Joseph Robert Nastasi
1,
Siyu Ma
1,
Shanmugam Alagappan
1,2,
Louwrens C. Hoffman
2 and
Daniel Cozzolino
2,*
1
School of Agriculture and Food Sustainability, The University of Queensland, Brisbane, QLD 4072, Australia
2
Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD 4072, Australia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(16), 7131; https://doi.org/10.3390/app14167131
Submission received: 1 July 2024 / Revised: 29 July 2024 / Accepted: 11 August 2024 / Published: 14 August 2024
(This article belongs to the Special Issue Food Chemistry, Analysis and Innovative Production Technologies)
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Abstract

:
Edible insects have been evaluated as an alternative and sustainable source of protein because of their nutritive and functional properties for humans and domestic animals. The objective of this study was to assess the use of chemometric [principal component analysis (PCA) and partial least squares (PLS)] combined with Rapid Visco Analyser (RVA) profiles to evaluate the addition of cricket powder (CKP) to chickpea (CPF) and flaxseed (FxF) flours. The results of this study showed that the addition of CKP powder to both CPF and FxF flours affects the pasting properties of the samples; in particular, a reduction in the peak (PV) and final viscosity (FV) was observed. The use of chemometric data techniques such as PCA and PLS regression allowed for a better interpretation of the RVA profiles. Both PCA and PLS regression allowed to qualitative and quantitatively identify the addition level of CKP powder to CPF and FxF flour samples. Differences in the PLS loadings associated with the RVA profile due to the addition of cricket powder were observed. The development of these methodologies will provide researchers and the food industry with better tools to both improve and monitor the quality of ingredients with functional properties as well as to further understand the use of insects as alternative sources of protein.

1. Introduction

The nutritional and functional value of plant food products depends not only on their botanical origin but also on the technological processes or transformation, which can be achieved by either the use of biological (e.g., enzyme treatment) or physical methods (e.g., extrusion) [1,2]. In addition to nutritive value (e.g., macro- and micronutrients), the pasting properties and the digestibility of food ingredients and products are important parameters for evaluating the functionality and nutritional value of food and food mixtures [1,2,3].
Edible insects have been evaluated and utilised as an alternative source of proteins for human consumption and animal feeds because of their nutrient composition and functionality [1,2,3]. The utilisation of insects as an alternative source of protein has increased, and research and development in this sector have grown in recent years [1,2,3]. Insects such as mealworms, buffalo worms, and crickets have been used in wheat bread fortification to improve the nutritive and functional value of bread and gluten-free bread, as well as to increase the protein content of muffins [2,3]. It has been also reported that the incorporation and blending of insects into cereal flour can improve the aroma of a food product and modify its texture, including an increase in hardness or improvements in consistency [2]. Insect flour can also contain chitin and chitosan, which inhibit the growth of some microorganisms; however, this might have an influence on the functional properties of the food [2].
The rapid visco analyser (RVA) is an empirical instrument used to measure the pasting properties of starch and starchy foods [4,5,6,7], but it may also be used to evaluate the pasting and gelatinisation properties of animal feeds [8]. In simple terms, the RVA is a heating and cooling viscometer that measures the viscosity of a sample across a standard or time protocol (usually a few minutes) while the sample is stirred [4,9,10]. The RVA is mainly used to determine the pasting process of the starch in a sample (e.g., pure starch or flour) by mixing the sample with water and forming a slurry that is subjected to stirring and heat [10,11]. During this process, a gel is formed as the starch granules swell and totally dislocate following gelatinisation [10,11]. At the end of the analysis, an RVA profile is generated, which is comprised of different parameters such as peak viscosity (PV), breakdown (BKD), setback (STB), holding strength (HS), and final viscosity (FV) [10,11].
The RVA instrument is generally utilised in the routine analysis of cereals to estimate eating quality and other starch processing parameters [5]. However, this instrument can be utilised not only to define the pasting properties of cereals and starchy foods but also in other food applications including simulating the processing conditions in a brewhouse [6], mimicking the mashing process [12], and evaluating viscosity changes associated with starch gelatinisation, proteolysis, and saccharification in different food ingredients and products [12]. Moreover, it has been used to estimate the functional properties of starch and protein during the processing of whole grain ingredients and products [13], to evaluate barley grain and malt quality [14], and to predict rice cooking quality [15].
Most recently, the utilisation of chemometric data analysis has been shown to improve the analytical power of the RVA by incorporating techniques such as principal component analysis (PCA) and partial least squares (PLS) regression [5,12,16,17,18]. In addition, the use of different pre-processing techniques to enhance the signal-to-noise ratio of the profile, such as derivatives (e.g., first- and second derivatives), has been evaluated and tested to improve the interpretation of RVA profiles [5,12,16,17,18]. Most of the current studies reported the addition of insect powders to cereal flours (e.g., wheat, corn); however, no reports were found on the addition of insect powder to pulses such as chickpeas or flaxseed meal.
The objective of this study was to assess the use of chemometric [principal component analysis (PCA) and partial least squares (PLS)] combined with RVA profiles to evaluate the addition of cricket powder (CKP) to chickpea (CPF) and flaxseed (FxF) flours.

2. Materials and Methods

2.1. Samples and Blends

The blends used in this study were prepared by adding commercial cricket (Acheta domesticus) powder (CKP) to commercial chickpea (Cicer arietinum) (CPF) and flaxseed al (Linum usitatissimum) flours (FxF). Batches (n = 3) of commercial CKP (approx. 1 kg each) were obtained as cricket protein powder (Hoppa Foods Pty Limited, Wollongong, NSW, Australia). Commercial CPF (n = 3) (desi type) and FxF (n = 3) (brown seeds) (approx. 1 kg each) were procured from local supermarkets in the Brisbane area (Queensland, Australia). A subsample from each batch was used to create binary mixtures using the following combinations: 100:0 % (pure flour no addition of other flour samples), 95:5 % w/w, 90:10 % w/w, 85:15 % w/w, 80:20 % w/w, 75:25 % w/w, 70:30 % w/w, 65:35 % w/w, 60:40 % w/w, and 50:50 % w/w. The blending protocol was the same for the different mixtures developed. For example, the 100 % CPF and 0 % CKP mixtures were made using 5 g w/w CPF and 0 g w/w CKP, and so forth. In this study, only the high levels of adulteration between the insect powder and plant flours were considered during the analysis. In addition, samples were analysed for crude protein (CP), crude fat (CF) and total fibre (TF) content using proximate analysis. The proximate analyses were carried out by the analytical services unit of the School of Agriculture and Food Sustainability, The University of Queensland (St. Lucia Campus, Brisbane, QLD, Australia). The association of official analytical collaboration (AOAC) methods 960.39 and 992.15 were used to determine crude fat (CF) and crude protein (CP). The proximate composition obtained was as follows: CKP had 60% CP, 17% CF, and 23% TF, CPF had 21% CP and 8% CF, and FxF had 18% and 42% CP and CF, respectively.

2.2. Pasting Properties (RVA Analysis)

Pure samples and blends (approximately 3 g, corrected using the moisture content of the sample, ±0.01 g) were slurred with distilled water (approximately 25 g as a function of the amount of adjusted sample, ±0.1 g) in an aluminium RVA canister (RVA 4500, PerkinElmer, Shelton, CT, USA). The sample was agitated by raising and lowering the plastic paddle through the aluminium canister before inserting the can into the RVA instrument. The standard RVA profile started with a temperature of 50 °C, held for 1 min, raised to 90 °C in 4 min, held for 10 min, cooled to 50 °C in 1 min, and held for 1 min, with a stirring speed of 160 rpm for the remainder of the test) (RVA 4500, PerkinElmer, Shelton, CT, USA).
The RVA instrument (RVA 4500, PerkinElmer, USA) was operated by Thermocline software (TCW3) for Windows (v. 3.11, NewPort Scientific, Sydney, NSW, Australia). At the end of the analysis, peak (PV) and final viscosity (FV), breakdown (BKD), setback (STB), time to peak, and pasting temperature were calculated by the software (TCW3, v. 3.11, NewPort Scientific, Sydney, NSW, Australia).

2.3. Data Analysis

After analysis, the RVA data were exported from TCW3 software in Excel format into SIMCA 17 software (Umetrics, Goettingen, Germany) for principal component analysis (PCA) and into Unscrambler X software (v 11, CAMO ASA, Oslo, Norway) for partial least squares (PLS) regression analysis. Both PCA and PLS models were validated using cross-validation (leave-one-out) [19]. The RVA profiles were pre-processed using the Savitzky–Golay second derivative (second polynomial order and 21 smoothing points) [5,18] prior to PCA and PLS regression. Partial least squares (PLS) regression was used to predict the addition level of cricket powder to CPF or FxF flours. The PLS regression models were evaluated by the standard error in cross-validation (SECV), the coefficient of determination in cross-validation (R2), bias, and slope and validated using cross-validation (leave-one-out) [5,18].

3. Results and Discussion

3.1. RVA Profiles

Figure 1 shows the RVA profiles of the different samples (blend and pure flour), where the key pasting parameters are reported in Table 1. It was observed that the CKP powder did not present any noticeable feature in the pasting profile when compared with the CPF and FxF flour samples. Specifically, pure CKP powder had no significant increase or decrease in viscosity in response to heating and cooling (Figure 1A—lightest grey line). CKP powder lacks any pasting potential because of the absence of starch or long polysaccharide structures with high water binding capacity similar to starch [20]. Furthermore, there is no crystalline packing of polymer chains similar to that of starch granules to deliver a rapid increase in viscosity post-gelatinisation.
An increase in PV and FV was observed as the addition of either CPF or FxF flour increased in the blends with CKP powder (Figure 1A,B). Overall, the addition of CKP powder into CPF and FxF flour blends had a negative effect on the pasting properties of these flours. For example, the addition of CKP powder resulted in a decrease in PV and BKD in both blends (see Figure 1A—CPF and Figure 1B—FxF). This observation can be explained by the high percentage of protein or chitin in the CKP blend (Figure 1). A higher percentage of protein in the mixture can restrict the swelling power of starch granules as insect powders contain a high concentration of chitin [21]. Furthermore, it has been reported that the viscosity of insect and cereal flour blends decreases as the proportion of chitin increases [21]. The addition of cricket powder with high a concentration of chitin resulted in low PV and BKD values of the blend samples analysed [21]. CKP powder also contains a high proportion of fat (17%), which can influence the formation of an amylose–lipid complex when starch and lipids are heated together, as reported by other authors [12]. Other authors also reported that an increase in viscosity might be associated with the interaction between amylose and lipids, causing the melting of the amylose–lipid complex [12]. A recent study reported that the PV values of corn flour decreased with a decrease in starch concentration. The same authors, however, observed that when insect powder was added to the corn blends, PV decreased compared with samples in which the same amount of starch was present [1]. This reduction in PV can be explained not only by the decrease in starch but also by the proportion of lipids that prevent the absorption of moisture and increase the free energy required for gelatinisation by forming a hydrophobic layer outside the starch granule [1]. These authors reported that PV decreased with higher levels of substitution with insect powder in the blends (10% and 15% substitution) [1]. This phenomenon was easily observed in our study within the FxF flour and CKP powder blends because the pasting profile does not show a rapid onset of increasing viscosity between 0 and 5 min, which is typically observed in flour or meal samples high in starch and low in fat [15]. The same trend was observed for both BKD and STB and, specifically, viscosity decreased with an increasing level of cricket in the blend [1]. The lower STB viscosity due to the addition of cricket flour indicates lower gel stability and lower sensitivity to retrogradation, probably caused by the more difficult re-association of the starch molecules because of the higher protein and lipid content, as reported by other authors [1,22].
In a study by Mohammed and collaborators (2014) [23], they suggested that the pasting temperature of wheat flour increased with the addition of chickpea flour. Furthermore, the chickpea flour has a higher amount of lipids, which may be explained the observed decrease in the PV of the blend. The same authors reported that both FV and STB significantly increased with the increasing level of mealworm powder [23]. The high FV and STB viscosity indicated a high level of retrogradation of the sample, which occurs at lower temperatures. Retrogradation of starch, followed by syneresis, is the main cause of the increase in hardness of bread products, which influences shelf-life and consumer acceptance [24].
The pasting properties of the FxF and CPF flour mixtures were also investigated to understand their techno-functional properties compared with the CKP powder (Figure 1C). It was observed that as the concentration of CPF powder increased in the FxF flour blends, there was an increase in PV and FV. However, blends higher in FxF flour had an instant increase in viscosity after heating, which is a result of a higher concentration of free soluble fibre in the FxF flour samples [1,22].

3.2. Principal Component Analysis

The RVA profiles were analysed using PCA to investigate underlying relationships in the data set and better interpret the patterns in the pasting curves. Figure 2 shows the PCA (panel A, score plot; panel B, loadings) from the analysis of the flour and powder samples (CPF, CKP, and FxF) and the corresponding blend samples analysed using the RVA. It was observed that the first two principal components (PCs) explained 99.3% of the total variance. For PCA models, a Q2 value can be used to interpret the predictiveness of a component, and a Q2 value close to that of R2 indicates good predictability. The PCA model had an R2 and Q2 of both 0.993, which indicates excellent model reliability and that the model can be applied to interpret the effect that CKP powder has on the pasting properties of the FxF and CPF flour blends.
The PCA scores reveal the patterns in the samples associated with the increase in CKP powder in the blends (Figure 2A). The first PC (89%) is associated with the increase in CPF flour in the blend, while PC2 (10%) is associated with the increase in FxF flour in the mix. The highest loadings in PC1 can be observed around 12 min, associated with FV, whereas the highest loadings in PC2 were observed around 3 min and 11 min, both associated with PV and FV, respectively. Because the highest loading was observed at approximately 12 min, the score plot was colour-scaled by the viscosity at 12.5 min (associated with FV) to assist in the visual interpretation of the model. The colour scaling indicates that the highest FV is observed within the FxF and CPF flour blends, and the lowest FV is observed within the CPF flour and CKP powder blends. Therefore, CKP powder is a suitable addition to FxF flour to modulate FV and may be added to CPF blends to modulate PV (Figure 1). This information is highly valuable for the bulk transfer of feed ingredients during processing, where it is important to account for viscosity changes while feed ingredients are pumped through pipes, mixed, and subject to either heating or cooling. Overall, Figure 2 demonstrates that by using a PCA score plot, one can easily and visually assess the pasting properties of a high number of flour blends fortified with insect powder, interpret techno-functional properties, and make informed decisions for processing applications.

3.3. Partial Least Squares Regression

The PLS regression was used to predict the addition level of CKP powder to the CPF and FxF flour samples based on their RVA pasting profiles. The cross-validation statistics are reported in Table 2. In the case of the addition of CKP powder to CPF flour, R2CV and SECV were 0.95 and 7.7, respectively. For the addition of CKP powder to FxF flour, R2CV and SECV were 0.90 and 10.9, respectively. The PLS regression statistics for the mix of the two plant flour samples, FxF and CPF, were R2CV = 0.94 and SECV = 5.7. The cross-validation statistics were very similar between the models; however, differences were observed in the loadings. The loadings for predicting the addition level of CKP powder into the CPF and FxF flour blends are shown in Figure 3. The highest loadings were observed around 4.5, 10.5, and 11.5 min for the addition of CKP powder to the CPF flour, while the highest and negative loadings for the blends of FxF flour and CKP powder were observed around 10.8 min. These time points correspond to changes in PV and FV. For the mixtures between FxF and CPF, the highest loadings were observed at 4.2, 7.6, and 9.9 min. These time points correspond to PV, onset of BKD, STB, and the increase towards FV. The PLS regression statistics indicate that the addition of CKP powder to either CPF or FxF blends can be reliably modelled, and loading plots can be used to interpret changes in the key parameters of the RVA pasting curve. Therefore, the application of PLS modelling is highly applicable for monitoring the pasting parameters of flour and meal blends fortified with insect powders. In addition, the PLS models allowed us to identify the addition level of insect flour to other plant protein sources. The results from such models can be used for optimising the ratio of insect powders in animal feed blends that are often heated, cooled, and pumped at processing facilities or via on-farm feeding machines [25]. Furthermore, these key parameters may also be correlated with the digestibility [26] and the glass transition of the samples, which influences shelf stability [27,28,29,30].

4. Conclusions

The results of this study showed that the addition of CKP powder to CPF and FxF flour affects the pasting properties of the flours and blends, mainly due to a reduction in PV and FV. The use of RVA analysis and the interpretation of the loadings can be used to study differences in the pasting properties of blends between insects and plant proteins. Furthermore, PCA models may be used to rapidly evaluate large data sets of pasting curves, which can decrease the time taken for processing decisions related to the flow of feed ingredients during processing. The development of these methodologies will provide researchers and industry with better tools to improve and better monitor the quality of ingredients with functional properties and to better understand the use of alternative proteins.

Author Contributions

S.M., formal analysis; S.A. and J.R.N., writing, review original and draft; D.C. and J.R.N., conceptualization, methodology, data analysis, writing—original draft preparation; L.C.H. and D.C., writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

Internal University of Queensland Institutional funds.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The support of the University of Queensland and Queensland Alliance for Agriculture and Food Innovation is acknowledged.

Conflicts of Interest

The authors declare no conflicts of interest.

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Applsci 14 07131 g001
Figure 1. RVA profile of binary mixtures of CPF and CFK (A), FxF and CKF (B), and FxF and CPF (C). A colour gradient is applied to each graph to signify a decrease in the concentration of either CPF or FxF. The range of the binary mixture is from 100:0 % w/w (pure flour no addition of other flour) to 0:100 % w/w. CPF = chickpea flour, CKF = cricket powder, and FxF = flaxseed meal flour.
Figure 1. RVA profile of binary mixtures of CPF and CFK (A), FxF and CKF (B), and FxF and CPF (C). A colour gradient is applied to each graph to signify a decrease in the concentration of either CPF or FxF. The range of the binary mixture is from 100:0 % w/w (pure flour no addition of other flour) to 0:100 % w/w. CPF = chickpea flour, CKF = cricket powder, and FxF = flaxseed meal flour.
Applsci 14 07131 g001
Applsci 14 07131 g002
Figure 2. Principal component scores (A) and loadings (B) derived from the RVA analysis of insect blends and pure flours. Different pasting curves are represented as circles on the score plot. The circles are colour-scaled from high (red) to low (blue) for their final viscosity measurements to aid in visual interpretation. Two significant PCs were generated for this model, where the following variance was explained: PC1: 89%, PC2: 10%. R2 = 99.3% and Q2 = 99.3%. Figure 2B loading corresponds to PC1 and the blue line corresponds to PC2.
Figure 2. Principal component scores (A) and loadings (B) derived from the RVA analysis of insect blends and pure flours. Different pasting curves are represented as circles on the score plot. The circles are colour-scaled from high (red) to low (blue) for their final viscosity measurements to aid in visual interpretation. Two significant PCs were generated for this model, where the following variance was explained: PC1: 89%, PC2: 10%. R2 = 99.3% and Q2 = 99.3%. Figure 2B loading corresponds to PC1 and the blue line corresponds to PC2.
Applsci 14 07131 g002
Applsci 14 07131 g003
Figure 3. Partial least squares loadings derived from the predicted addition level of cricket powder to chickpea and flax meal flour analysed using the RVA.
Figure 3. Partial least squares loadings derived from the predicted addition level of cricket powder to chickpea and flax meal flour analysed using the RVA.
Applsci 14 07131 g003
Table 1. Pasting parameters of chickpea (CPF) flour, flaxseed (FxF) flour, and cricket powder (CKP).
Table 1. Pasting parameters of chickpea (CPF) flour, flaxseed (FxF) flour, and cricket powder (CKP).
CPFFxFCKP *
Peak viscosity (PV)710 ± 23855 ± 22N/A
Final viscosity (FV)189 ± 5984 ± 33N/A
Breakdown (BKD)13 ± 7−57 ± 2N/A
Setback (STB)162 ± 6737 ± 31N/A
Time to peak (min)77.1N/A
Pasting temperature (°C)6550N/A
* Please note that CKP has no pasting potential.
Table 2. Cross-validation statistic for the predicted addition level of cricket flour to chickpea and flax meal flours analysed using the RVA.
Table 2. Cross-validation statistic for the predicted addition level of cricket flour to chickpea and flax meal flours analysed using the RVA.
CPF-CKPFxF-CKPFxF-CPF
R2CV0.950.900.94
SECV7.710.95.7
Bias0.090.820.12
slope0.90.870.94
LVs436
CPF-CKP: chickpea flour and cricket powder; FxF-CKP: flaxseed flour and cricket powder; FxF-CPF: chickpea and flaxseed flour; R2CV: coefficient of determination in cross-validation, LVs: latent variables, SECV: coefficient of cross-validation.
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Nastasi, J.R.; Ma, S.; Alagappan, S.; Hoffman, L.C.; Cozzolino, D. Assessment of the Addition of Cricket (Acheta domesticus) Powder to Chickpea (Cicer arietinum) and Flaxseed (Linum usitatissimum) Flours: A Chemometric Evaluation of Their Pasting Properties. Appl. Sci. 2024, 14, 7131. https://doi.org/10.3390/app14167131

AMA Style

Nastasi JR, Ma S, Alagappan S, Hoffman LC, Cozzolino D. Assessment of the Addition of Cricket (Acheta domesticus) Powder to Chickpea (Cicer arietinum) and Flaxseed (Linum usitatissimum) Flours: A Chemometric Evaluation of Their Pasting Properties. Applied Sciences. 2024; 14(16):7131. https://doi.org/10.3390/app14167131

Chicago/Turabian Style

Nastasi, Joseph Robert, Siyu Ma, Shanmugam Alagappan, Louwrens C. Hoffman, and Daniel Cozzolino. 2024. "Assessment of the Addition of Cricket (Acheta domesticus) Powder to Chickpea (Cicer arietinum) and Flaxseed (Linum usitatissimum) Flours: A Chemometric Evaluation of Their Pasting Properties" Applied Sciences 14, no. 16: 7131. https://doi.org/10.3390/app14167131

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