Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation
2.2. Hyperspectral Imaging System (HIS)
2.3. Image Calibration
2.4. Spectral Extraction
2.5. Data Analysis
2.5.1. Spectral Pre-Processing
2.5.2. DD-SIMCA
2.5.3. Partial Least Square Regression (PLSR)
2.5.4. Chemical Mapping Based on the Image
3. Results and Discussion
3.1. Spectral Profile of Almond and Adulterants
3.2. Class Modeling Construction Based on DD-SIMCA and Validation Performance
3.3. Reproducibility with an External Validation Set
3.4. PLSR Model Development for Almond Adulterants
3.5. Spatial Distribution Maps of Adulterants in Almond Powder
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Number of Samples | Maximum | Minimum | Mean ± SD | ||
---|---|---|---|---|---|
DD-SIMCA | Calibration | 100 | 0 | 0 | 0 ± 0 |
Validation1 | 110 | 50 | 0 | 25.04 ± 15.94 | |
Validation2 | 50 | 30 | 0 | 14.8 ± 10.72 | |
PLSR | Calibration | 66 | 50 | 0 | 25 ± 15.94 |
Validation1 | 44 | 50 | 0 | 25 ± 15.99 | |
Validation2 | 44 | 30 | 0 | 13.45 ± 11.07 |
Sensitivity (%) | Number of Correctly Classified Samples/Total Number of Samples | Specificity (%) | Number of Correctly Classified Samples/Total Number of Pure Samples | Accuracy (%) | |
---|---|---|---|---|---|
Val-1st-AA | 95 | 95/100 | 100 | 10/10 | 95.5 |
Val-2nd-AA | 100 | 40/40 | 100 | 10/10 | 100.0 |
Val-1st-AP | 89 | 89/100 | 100 | 10/10 | 90.0 |
Val-2nd-AP | 90 | 36/40 | 100 | 10/10 | 92.0 |
Pre-Processing Methods | 1st Variety Adulterated with Apricot | 2nd Variety Adulterated with Apricot | 1st Variety Adulterated with Peanut | 2nd Variety Adulterated with Peanut | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
R2cal | SEC (%) | R2pre | SEP (%) | R2pre | SEP (%) | R2cal | SEC (%) | R2pre | SEP (%) | R2pre | SEP (%) | |
Mean Normalization | 0.99 | 1.16 | 0.98 | 1.92 | 0.99 | 1.42 | 0.99 | 1.83 | 0.83 | 6.56 | 0.76 | 5.21 |
Max Normalization | 0.99 | 0.76 | 0.99 | 1.00 | 0.99 | 0.80 | 0.98 | 2.20 | 0.81 | 7.05 | 0.75 | 5.35 |
Range Normalization | 0.99 | 1.53 | 0.98 | 2.13 | 0.96 | 10.9 | 0.95 | 3.45 | 0.82 | 6.65 | 0.76 | 5.32 |
MSC | 0.99 | 1.28 | 0.98 | 1.81 | 0.98 | 2.28 | 0.98 | 2.17 | 0.75 | 8.21 | 0.56 | 7.07 |
SNV | 0.99 | 0.85 | 0.99 | 1.16 | 0.98 | 4.11 | 0.98 | 2.17 | 0.75 | 8.16 | 0.56 | 7.13 |
SG 1st Derivatives | 0.99 | 0.79 | 0.99 | 0.95 | 0.98 | 1.38 | 0.99 | 1.68 | 0.98 | 2.18 | 0.94 | 4.77 |
SG 2nd Derivatives | 0.99 | 0.71 | 0.99 | 0.71 | 0.99 | 0.93 | 0.99 | 1.48 | 0.97 | 2.53 | 0.91 | 4.38 |
Raw | 0.99 | 0.75 | 0.99 | 0.87 | 0.99 | 1.10 | 0.98 | 1.00 | 0.98 | 2.40 | 0.90 | 5.71 |
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Faqeerzada, M.A.; Lohumi, S.; Kim, G.; Joshi, R.; Lee, H.; Kim, M.S.; Cho, B.-K. Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method. Sensors 2020, 20, 5855. https://doi.org/10.3390/s20205855
Faqeerzada MA, Lohumi S, Kim G, Joshi R, Lee H, Kim MS, Cho B-K. Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method. Sensors. 2020; 20(20):5855. https://doi.org/10.3390/s20205855
Chicago/Turabian StyleFaqeerzada, Mohammad Akbar, Santosh Lohumi, Geonwoo Kim, Rahul Joshi, Hoonsoo Lee, Moon Sung Kim, and Byoung-Kwan Cho. 2020. "Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method" Sensors 20, no. 20: 5855. https://doi.org/10.3390/s20205855