Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy
Abstract
:1. Introduction
2. Materials and Methods
2.1. Samples and Preparation
2.2. Nanopacking Material
2.3. FT-NIR Spectroscopy
2.4. Quantitative Analysis of Surface Color, Weight Loss Rate, Vitamin C and Firmness
2.5. Freshness Levels Description
2.6. Data Processing
2.7. Evaluation of Models
2.8. Statistical Analysis
3. Results and Discussion
3.1. Analysis of Surface Color, Weight Loss Rate, Vitamin C and Firmness for Mini-Chinese Cabbage during Storage
3.2. Spectral Analysis
3.3. Prediction Performance of Surface Color and Quality Attributes Based on FT-NIR Dataset
3.4. Classification Performance of Freshness Levels Based on FT-NIR Dataset
3.5. Independent Test-Set Validation of Freshness Level in Mini-Chinese Cabbage
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Freshness Levels | Description |
---|---|
Level 1 | Surface without visible defects, smells fresh. Quality attributes including weight loss rate < 30%, L* > 71 and Vc content > 59 mg/100 g. |
Level 2 | Surface with visible defect points or peculiar smells. Quality attributes including 30% ≤ weight loss rate < 50%, 68 < L* ≤ 71 and 47 < Vc content ≤ 59 mg/100 g. |
Level 3 | Surface with visible defect areas and unpleasant smell. Quality attributes including weight loss rate ≥ 51%, L* ≤ 68 and Vc content ≤ 47 mg/100 g. |
Quality Attributes | Pretreatment | Model | Calibration | Cross-Validation | Prediction | ||||
---|---|---|---|---|---|---|---|---|---|
RC2 | RMSEC | RCV2 | RMSECV | Rp2 | RMSEP | RPD | |||
Weight loss rate | SNV | PLSR | 0.95 | 1.334 | 0.92 | 1.339 | 0.96 | 1.332 | 3.612 |
1-st | 0.90 | 1.340 | 0.86 | 1.573 | 0.87 | 1.360 | 2.191 | ||
2-nd | 0.82 | 1.654 | 0.80 | 1.732 | 0.80 | 1.564 | 3.212 | ||
MSC | 0.88 | 1.365 | 0.85 | 1.537 | 0.88 | 1.354 | 2.435 | ||
Autoscale | 0.86 | 1.573 | 0.81 | 1.691 | 0.84 | 1.476 | 2.830 | ||
SNV | SVR | 0.83 | 1.643 | 0.80 | 1.733 | 0.79 | 1.590 | 2.700 | |
1-st | 0.89 | 1.378 | 0.87 | 1.520 | 0.87 | 1.359 | 2.191 | ||
2-nd | 0.85 | 1.587 | 0.81 | 1.720 | 0.83 | 1.489 | 2.795 | ||
MSC | 0.87 | 1.520 | 0.82 | 1.647 | 0.85 | 1.435 | 2.546 | ||
Autoscale | 0.87 | 1.489 | 0.82 | 1.649 | 0.85 | 1.461 | 2.544 | ||
Firmness | SNV | PLSR | 0.58 | 2.542 | 0.50 | 2.714 | 0.57 | 2.606 | 1.437 |
1-st | 0.60 | 2.403 | 0.51 | 2.704 | 0.58 | 2.578 | 2.066 | ||
2-nd | 0.57 | 2.604 | 0.49 | 2.821 | 0.44 | 2.706 | 1.128 | ||
MSC | 0.51 | 2.704 | 0.44 | 2.781 | 0.51 | 2.695 | 2.042 | ||
Autoscale | 0.45 | 2.775 | 0.40 | 3.305 | 0.40 | 2.789 | 1.195 | ||
SNV | SVR | 0.60 | 2.463 | 0.51 | 2.812 | 0.57 | 2.598 | 1.608 | |
1-st | 0.55 | 2.671 | 0.48 | 2.901 | 0.50 | 2.701 | 2.159 | ||
2-nd | 0.58 | 2.534 | 0.47 | 2.953 | 0.49 | 2.735 | 2.124 | ||
MSC | 0.56 | 2.638 | 0.41 | 3.217 | 0.48 | 2.780 | 2.091 | ||
Autoscale | 0.60 | 2.479 | 0.55 | 2.671 | 0.60 | 2.453 | 2.205 | ||
Vitamin C | SNV | PLSR | 0.90 | 3.213 | 0.85 | 3.474 | 0.86 | 3.43 | 2.727 |
1-st | 0.91 | 3.231 | 0.84 | 3.481 | 0.89 | 3.25 | 2.883 | ||
2-nd | 0.81 | 3.500 | 0.75 | 4.002 | 0.80 | 3.46 | 2.015 | ||
MSC | 0.90 | 3.113 | 0.85 | 3.474 | 0.95 | 3.19 | 2.681 | ||
Autoscale | 0.87 | 3.453 | 0.74 | 4.110 | 0.78 | 3.48 | 2.113 | ||
SNV | SVR | 0.82 | 3.496 | 0.74 | 4.024 | 0.79 | 3.47 | 2.238 | |
1-st | 0.86 | 3.467 | 0.81 | 3.500 | 0.87 | 3.24 | 2.513 | ||
2-nd | 0.88 | 3.405 | 0.80 | 3.557 | 0.87 | 3.25 | 2.512 | ||
MSC | 0.89 | 3.436 | 0.81 | 3.501 | 0.85 | 3.46 | 2.442 | ||
Autoscale | 0.90 | 3.298 | 0.80 | 3.557 | 0.82 | 3.31 | 2.379 |
Quality Attributes | Pretreatment | Model | Calibration | Cross-Validation | Prediction | ||||
---|---|---|---|---|---|---|---|---|---|
RC2 | RMSEC | RCV2 | RMSECV | Rp2 | RMSEP | RPD | |||
L* | SNV | PLSR | 0.72 | 2.571 | 0.68 | 2.823 | 0.70 | 2.324 | 2.458 |
1-st | 0.69 | 2.803 | 0.62 | 3.074 | 0.67 | 2.415 | 2.258 | ||
2-nd | 0.74 | 2.472 | 0.67 | 2.854 | 0.71 | 2.183 | 2.015 | ||
MSC | 0.77 | 2.372 | 0.68 | 2.823 | 0.72 | 2.051 | 2.453 | ||
Autoscale | 0.65 | 2.762 | 0.60 | 3.227 | 0.60 | 2.903 | 2.469 | ||
SNV | SVR | 0.74 | 2.586 | 0.64 | 2.974 | 0.65 | 2.372 | 1.541 | |
1-st | 0.73 | 2.594 | 0.65 | 2.914 | 0.74 | 2.134 | 2.445 | ||
2-nd | 0.68 | 2.908 | 0.61 | 3.146 | 0.60 | 2.961 | 2.098 | ||
MSC | 0.84 | 2.051 | 0.74 | 2.586 | 0.82 | 2.013 | 3.069 | ||
Autoscale | 0.80 | 2.162 | 0.71 | 2.584 | 0.75 | 2.122 | 2.483 | ||
a* | SNV | PLSR | 0.75 | 1.241 | 0.68 | 1.586 | 0.72 | 1.309 | 2.088 |
1-st | 0.70 | 1.443 | 0.62 | 1.733 | 0.68 | 1.528 | 1.781 | ||
2-nd | 0.68 | 1.587 | 0.61 | 1.748 | 0.64 | 1.691 | 1.332 | ||
MSC | 0.71 | 1.401 | 0.62 | 1.733 | 0.68 | 1.528 | 1.781 | ||
Autoscale | 0.74 | 1.287 | 0.67 | 1.593 | 0.69 | 1.501 | 1.855 | ||
SNV | SVR | 0.77 | 1.032 | 0.71 | 1.403 | 0.73 | 1.288 | 2.145 | |
1-st | 0.72 | 1.317 | 0.68 | 1.557 | 0.70 | 1.402 | 1.987 | ||
2-nd | 0.67 | 1.594 | 0.60 | 1.756 | 0.65 | 1.625 | 1.501 | ||
MSC | 0.72 | 1.317 | 0.67 | 1.594 | 0.70 | 1.402 | 1.987 | ||
Autoscale | 0.75 | 1.243 | 0.68 | 1.557 | 0.71 | 1.388 | 2.051 | ||
b* | SNV | PLSR | 0.80 | 1.211 | 0.71 | 1.302 | 0.78 | 1.278 | 1.943 |
1-st | 0.85 | 1.204 | 0.72 | 1.283 | 0.85 | 1.264 | 2.432 | ||
2-nd | 0.79 | 1.236 | 0.72 | 1.289 | 0.72 | 1.323 | 1.893 | ||
MSC | 0.81 | 1.218 | 0.73 | 1.301 | 0.73 | 1.312 | 1.897 | ||
Autoscale | 0.68 | 1.324 | 0.61 | 1.374 | 0.67 | 1.421 | 1.457 | ||
SNV | SVR | 0.73 | 1.278 | 0.61 | 1.374 | 0.70 | 1.376 | 1.541 | |
1-st | 0.78 | 1.245 | 0.62 | 1.370 | 0.73 | 1.356 | 1.896 | ||
2-nd | 0.78 | 1.234 | 0.62 | 1.370 | 0.75 | 1.321 | 1.913 | ||
MSC | 0.80 | 1.216 | 0.72 | 1.289 | 0.74 | 1.310 | 1.906 | ||
Autoscale | 0.77 | 1.270 | 0.70 | 1.311 | 0.71 | 1.368 | 1.632 |
Models | Freshness Levels | Accuracy/% | |||
---|---|---|---|---|---|
Calibration | Level 1 | Level 2 | Level 3 | ||
Level 1 | 40 | 1 | 4 | 88.8 | |
Level 2 | 8 | 32 | 5 | 71.1 | |
Level 3 | 2 | 3 | 39 | 86.6 | |
Total accuracy/% | 82.1 | ||||
Prediction | Level 1 | 13 | 1 | 1 | 86.6 |
Level 2 | 1 | 11 | 3 | 73.3 | |
Level 3 | 0 | 3 | 12 | 80.0 | |
Total accuracy/% | 79.9 |
Models | Freshness Levels | Accuracy/% | |||
---|---|---|---|---|---|
Calibration | Level 1 | Level 2 | Level 3 | ||
Level 1 | 43 | 1 | 1 | 95.5 | |
Level 2 | 4 | 38 | 3 | 84.4 | |
Level 3 | 1 | 4 | 40 | 88.8 | |
Total accuracy/% | 89.6 | ||||
Prediction | Level 1 | 14 | 1 | 0 | 93.3 |
Level 2 | 0 | 13 | 2 | 86.6 | |
Level 3 | 0 | 2 | 13 | 86.6 | |
Total accuracy/% | 88.8 |
Models | Freshness Levels | Accuracy/% | |||
---|---|---|---|---|---|
SVC | Level 1 | Level 2 | Level 3 | ||
Level 1 | 38 | 6 | 1 | 84.4 | |
Level 2 | 7 | 34 | 4 | 75.6 | |
Level 3 | 0 | 6 | 39 | 86.7 | |
Total accuracy/% | 82.2% |
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Liu, Q.; Chen, S.; Zhou, D.; Ding, C.; Wang, J.; Zhou, H.; Tu, K.; Pan, L.; Li, P. Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy. Foods 2021, 10, 2309. https://doi.org/10.3390/foods10102309
Liu Q, Chen S, Zhou D, Ding C, Wang J, Zhou H, Tu K, Pan L, Li P. Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy. Foods. 2021; 10(10):2309. https://doi.org/10.3390/foods10102309
Chicago/Turabian StyleLiu, Qiang, Shaoxia Chen, Dandan Zhou, Chao Ding, Jiahong Wang, Hongsheng Zhou, Kang Tu, Leiqing Pan, and Pengxia Li. 2021. "Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy" Foods 10, no. 10: 2309. https://doi.org/10.3390/foods10102309