Recent Advances in Light Penetration Depth for Postharvest Quality Evaluation of Fruits and Vegetables
Abstract
:1. Introduction
2. The Theory of Absorption, Scattering and Penetration Depth
3. Light Transfer in Turbid Medium
3.1. Diffusion Approximation Theory
3.2. Monte Carlo Method
3.3. Adding-Doubling Method
4. Optical Detection Techniques
4.1. Time Resolved Spectroscopy
4.2. Spatially Resolved Spectroscopy
4.3. Frequency Domain Technique
4.4. Structured Illumination
4.4.1. Spatial Frequency Domain Imaging
4.4.2. Structured Illumination Reflectance Imaging
5. Applications
5.1. Analysis of Absorption and Scattering Coefficients
5.2. Maturity and Quality Assessment
Product | Technique | Year | Parameter | Wavelength (nm) | Model | Result | Reference |
---|---|---|---|---|---|---|---|
Apple | TRS | 2020 | Maturity | 540~1064 | Pearson correlation analysis | For chroma: r = −0.604, r = 0.615 | [102] |
SRS | 2020 | Maturity | 550~1650 | PLSDA | GD: Acc = 100% | [103] | |
SRS | 2021 | Firmness, SSC | 600~1000 | PLSDA | SSC: R2 = 0.92, Rmse = 0.35% Firmness: R2 = 0.87, Rmse = 0.71 N | [63] | |
SRS | 2021 | Flesh color | 190~1070 | PLSDA | Skin: R2 = 0.95 Whole flesh, R2 = 0.69 | [104] | |
SFDI | 2022 | SSC, firmness, color | 450~750 | SVM | Rp = 0.66, 0.73, 0.86 | [105] | |
Peach | SRS | 2020 | Soluble, TA | 550~1000 | Pearson correlation analysis | r = 0.898 r = −0.776 | [97] |
SRS | 2021 | Maturity | 550~1000 | SVM | Acc = 94.12%, 95.59% and 94.12% | [99] | |
SRS | 2022 | Firmness, SSC | 550~1650 | PLSDA | Rp = 0.853, Rmse = 14.76 N Rp = 0.839, Rmse = 0.5 | [90] | |
Kiwifruit | SRS | 2022 | Firmness, SSC, pH | 650~1000 | PLSDA | R2 = 0.64, 0.67, 0.38 Rmse = 3.63 N/cm2, 0.95%, 0.19% | [100] |
Mango | TRS | 2015 | Maturity | 540~900 | EP, FD | —— | [106] |
TRS | 2023 | Maturity, pulp color | 540 | Logistic/exponential model | = 99.8% | [57] | |
Tomato | SRS | 2018 | SSC, pH | 400~1100 900~1300 | PLSDA | For 400~1100: Rp = 0.729, 0.743 For 900~1300: Rp = 0.815, 0.741 | [101] |
SRS | 2018 | Firmness | 550~1650 | PLSDA | Rp = 0.859, Rmse = 1.00 | [66] | |
SRS | 2020 | Maturity | 550~1650 | SVM | Acc = 98.3% | [67] |
5.3. Defect Detection
5.4. Analysis of Light Penetration Depth
6. Challenges and Prospects
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Model | Advantage | Limitation |
---|---|---|
Diffusion approximation equation | Computationally fast and intuitive, wide applicability and suitable for strong scattering media | Unsuitable for low scattering media and unable to handle anisotropic scattering |
Monte Carlo method | High accuracy, flexibility and wide applicability | Large calculation, time-consuming and difficult to apply in real time |
Adding-doubling method | Suitable for anisotropic scattering and multilayer media With high accuracy and short calculation time, suitable for real-time applications | Limited to the assumption of uniform media, only suitable for planar layer media and unsuitable for extremely high scattering |
Technique | Advantage | Disadvantage |
---|---|---|
TRS | Good penetration ability, penetrating tissues as deep as about 1 to 2 cm | Requiring expensive and sophisticated instrumentation, as well as good contact between the sample and the detector |
SRS | Being cost-effective on instruments, high execution efficiency and support for a wide range of spectral range | Is insufficient for analyzing deep defects inside samples |
FD | Having high resolution and strong noise suppression ability, and the instrument used is cheaper than TRS | Requiring good contact between the sample and the detector, only one single frequency |
SFDI | Wide field non-contact, imaging depth discrimination and effective signal enhancement | Requiring selection of the feature band and cannot achieve real-time detection |
SIRI | Is capable of revealing subsurface tissue properties at specific depths and enhancing the visibility of defects | Being unsuitable for online inspection of food quality and limited to defect detection |
Product | Technique | Year | Wavelength (nm) | (cm−1) | (cm−1) | Reference |
---|---|---|---|---|---|---|
Apple | SRS | 2009 | 500~1000 | 0.04~2.52 | 1.02~12.61 | [31] |
SRS | 2014 | 500~1000 | 0~2.6 | [62] | ||
TRS | 2014 | 650~1050 | 0.02~0.45 | —— | [94] | |
SFDI | 2021 | 400~1000 | 0~1.2 | 0~35 | [95] | |
Peach | SRS | 2011 | 515~1000 | 0~0.5 | 5~20 | [96] |
SRS | 2020 | 600~1000 | 0.9~4.1 | 5.59~16.12 | [34] | |
SRS | 2020 | 550~1000 | 0~0.5 | 2~14 | [97] | |
IS | 2020 | 400~1050 | 0.3~4 | 2~12 | [52] | |
SRS | 2022 | 550~1650 | —— | —— | [90] | |
Pear | IS | 2023 | 500~1000 | 0~3 | 10~18 | [53] |
Kiwifruit | IS | 2021 | 950~1650 | 0~9 | 3~13 | [54] |
IS | 2022 | 500~1050 | 0~0.8 | 2~10 | [91] | |
Tomato | SRS | 2018 | 550~1300 | 0~2.5 | 0.8~6 | [64] |
Blueberry | IS | 2017 | 925~1400 | 0~10 | 0~8 | [93] |
Mango | TRS | 2023 | 540 | 0.103~0.403 0.106~0.511 | —— | [57] |
Melon | IS | 2024 | 450~1650 | 0~7.5 | 5~55 | [55] |
Product | Technique | Year | Type of Defect | Model | Accuracy | Reference |
---|---|---|---|---|---|---|
Apple | TRS | 2014 | Internal browning | LDA | 90%, 71% | [94] |
SRS | 2017 | Mealiness | ANN | 76%, 82% | [114] | |
SIRI | 2018 | Fresh bruising | BDA | 70%~100% | [83] | |
SIRI | 2018 | Surface and Subsurface defects | CNN | 98% | [85] | |
SFDI | 2021 | Early bruising | ANN | MAE = 0.18%, 0.027% Rmse = 0.01, 0.14 | [115] | |
SFDI | 2022 | Early bruising | —— | —— | [112] | |
SFDI | 2022 | Subsurface Bruising | cGANs | PSNR = 34.72 dB SSIM = 0.84 | [116] | |
SFDI | 2023 | Bruises | —— | —— | [113] | |
SFDI | 2023 | Early bruises | CNN | two-category: 99.16% three-category: 91.50% | [117] | |
Pear | SFDI | 2018 | Bruising | Discriminant analysis | 90%, 87.5% | [74] |
SFDI | 2021 | Surface damage | LDA | class 1: 92.5% class 2: 83.8% | [19] | |
SFDI | 2023 | Bruising | LSTMR | MAE = 0.32%, 0.21% | [20] | |
Peach | SIRI | 2019 | Early decay | CNN | 98.6%, 97.6% | [71] |
SRS | 2020 | Fungal infection | PLSDA | 88% | [34] | |
S-MSI | 2023 | Bruise detection | WSA | S1: 92% S2: 97.43% S3: 99.86% | [92] | |
Orange | SIRI | 2022 | Early decay | PLSDA | 96.4% | [86] |
Kiwifruit | SIRI | 2023 | Chilling injury | SVM | 94.2% | [118] |
Cucumber | SIRI | 2021 | Subsurface bruising | SVM | 94% | [87] |
Product | Technique | Year | Research Content | Result | Reference |
---|---|---|---|---|---|
Apple | SRS-MC | 2009 | Determine the optimum range of perception | δ is 0.43~8.67 cm over the 500~1000 nm; δ is related to wavelength | [31] |
MC | 2015 | Effects of skin thickness on light transport features in tissues | Thin skin has little effect on δ | [30] | |
TRS | 2020 | Nondestructive assessment of the internal quality of fruits | δ is related to SDS | [60] | |
SFDI-MC | 2020 2021 | Estimate and of two-layered horticultural products | Smaller frequency could generate larger δ | [24,121] | |
Peach | MC | 2015 | Effects of the core and the skin on light transport features | Pericarp has more influence on δ | [122] |
Citrus | —— | 2015 | Effects of skin thickness on light transport features in tissues | Thick pericarp has great influence on δ | [30] |
Melon | NIR | 2018 | Study the δ based on three SDS (10, 20 and 30 mm) | Depth ranges were 11.7~12.4, 14.2~15.8 and 16.8~21.0 mm | [123] |
VIS/NIR | 2024 | Investigate the light propagation through melons | δ is more than 2.5 mm in exocarp, 3 mm in green mesocarp and 3.5 mm in pulp | [55] | |
Pomelo | VIS/NIR | 2022 | Prediction of soluble solid content | δ determines the design of the detection system for large fruit | [33] |
Persimmon | HSI-SR | 2023 | Estimate the δ of a HSI system in a Vis-NIR configuration (in 450~1050 nm) | δ is limited to about 2 mm | [32] |
Turbid media | MC | 2012 | Influence of the illumination-collection geometry and optical properties on δ | δ is related to illumination-collection area and collection angle | [124] |
SRS-MC | 2016 | Estimate the optical properties of two-layer media | δ is proportional to SDS | [25] |
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Huang, Y.; Xiong, J.; Li, Z.; Hu, D.; Sun, Y.; Jin, H.; Zhang, H.; Fang, H. Recent Advances in Light Penetration Depth for Postharvest Quality Evaluation of Fruits and Vegetables. Foods 2024, 13, 2688. https://doi.org/10.3390/foods13172688
Huang Y, Xiong J, Li Z, Hu D, Sun Y, Jin H, Zhang H, Fang H. Recent Advances in Light Penetration Depth for Postharvest Quality Evaluation of Fruits and Vegetables. Foods. 2024; 13(17):2688. https://doi.org/10.3390/foods13172688
Chicago/Turabian StyleHuang, Yuping, Jie Xiong, Ziang Li, Dong Hu, Ye Sun, Haojun Jin, Huichun Zhang, and Huimin Fang. 2024. "Recent Advances in Light Penetration Depth for Postharvest Quality Evaluation of Fruits and Vegetables" Foods 13, no. 17: 2688. https://doi.org/10.3390/foods13172688