Chemometrics in tandem with near infrared (NIR) hyperspectral imaging and Fourier transform mid infrared (FT-MIR) microspectroscopy for variety identification and cooking loss determination of sweet potato

Wen-Hao Su, Serafim Bakalis, Da-Wen Sun

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6 Citations (Scopus)

Abstract

Near infrared (NIR) hyperspectral imaging and Fourier transform mid infrared (FT-MIR) microspectroscopy were explored in the current study to investigate how constituent elements of sweet potato change during cooking, and in the meantime, to identify sweet potato varieties. Partial least square discriminant analysis (PLSDA) model was established to classify varieties of sweet potato, and the correct classification rate of the PLSDA model using Spectral Set I (964–1645 nm) reached as high as 100%. Competitive adaptive reweighted sampling (CARS) was introduced to choose incipient feature wavelengths from three spectral subsets related to tuber cooking loss (CL). Based on 8 feature variables from Spectral Set I, CARS-SVMR model performed best with the highest coefficient of determination in prediction (R2P) of 0.893 and the lowest root mean square error of prediction (RMSEP) of 0.075. Then, these three subsets of feature wavelengths selected by CARS were re-optimised by using successive projections algorithm (SPA). With 7 feature variables from Spectral Set II (3996–600 cm−1) suggested by CARS-SPA, the CARS-SPA-PLSR model predicted tuber CL with R2P of 0.773 and RMSEP of 0.079. Moreover, the CARS-SPA-PLSR model using 5 wavelengths from Spectral Set I exhibited good prediction result, with R2P of 0.913 and RMSEP of 0.058. Although both techniques are capable of determining sweet potato CL in an effective way, the NIR technology demonstrates better predictive capability based on the reduced CARS-SPA-PLSR model.
Original languageEnglish
JournalBiosystems Engineering
Volume180
DOIs
StatePublished - Apr 30 2019

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Ipomoea batatas
chemometrics
Cooking
Infrared imaging
Fourier Analysis
cooking quality
sweet potatoes
potato
Fourier transform
near infrared
Fourier transforms
image analysis
Sampling
Infrared radiation
sampling
Discriminant Analysis
prediction
Mean square error
Least-Squares Analysis
wavelengths

Cite this

@article{7e8d16c451c44a4880332504c56b1eb2,
title = "Chemometrics in tandem with near infrared (NIR) hyperspectral imaging and Fourier transform mid infrared (FT-MIR) microspectroscopy for variety identification and cooking loss determination of sweet potato",
abstract = "Near infrared (NIR) hyperspectral imaging and Fourier transform mid infrared (FT-MIR) microspectroscopy were explored in the current study to investigate how constituent elements of sweet potato change during cooking, and in the meantime, to identify sweet potato varieties. Partial least square discriminant analysis (PLSDA) model was established to classify varieties of sweet potato, and the correct classification rate of the PLSDA model using Spectral Set I (964–1645 nm) reached as high as 100{\%}. Competitive adaptive reweighted sampling (CARS) was introduced to choose incipient feature wavelengths from three spectral subsets related to tuber cooking loss (CL). Based on 8 feature variables from Spectral Set I, CARS-SVMR model performed best with the highest coefficient of determination in prediction (R2P) of 0.893 and the lowest root mean square error of prediction (RMSEP) of 0.075. Then, these three subsets of feature wavelengths selected by CARS were re-optimised by using successive projections algorithm (SPA). With 7 feature variables from Spectral Set II (3996–600 cm−1) suggested by CARS-SPA, the CARS-SPA-PLSR model predicted tuber CL with R2P of 0.773 and RMSEP of 0.079. Moreover, the CARS-SPA-PLSR model using 5 wavelengths from Spectral Set I exhibited good prediction result, with R2P of 0.913 and RMSEP of 0.058. Although both techniques are capable of determining sweet potato CL in an effective way, the NIR technology demonstrates better predictive capability based on the reduced CARS-SPA-PLSR model.",
author = "Wen-Hao Su and Serafim Bakalis and Da-Wen Sun",
year = "2019",
month = "4",
day = "30",
doi = "10.1016/j.biosystemseng.2019.01.005",
language = "English",
volume = "180",
journal = "Biosystems Engineering",
issn = "1537-5110",
publisher = "Academic Press Inc.",

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TY - JOUR

T1 - Chemometrics in tandem with near infrared (NIR) hyperspectral imaging and Fourier transform mid infrared (FT-MIR) microspectroscopy for variety identification and cooking loss determination of sweet potato

AU - Su, Wen-Hao

AU - Bakalis, Serafim

AU - Sun, Da-Wen

PY - 2019/4/30

Y1 - 2019/4/30

N2 - Near infrared (NIR) hyperspectral imaging and Fourier transform mid infrared (FT-MIR) microspectroscopy were explored in the current study to investigate how constituent elements of sweet potato change during cooking, and in the meantime, to identify sweet potato varieties. Partial least square discriminant analysis (PLSDA) model was established to classify varieties of sweet potato, and the correct classification rate of the PLSDA model using Spectral Set I (964–1645 nm) reached as high as 100%. Competitive adaptive reweighted sampling (CARS) was introduced to choose incipient feature wavelengths from three spectral subsets related to tuber cooking loss (CL). Based on 8 feature variables from Spectral Set I, CARS-SVMR model performed best with the highest coefficient of determination in prediction (R2P) of 0.893 and the lowest root mean square error of prediction (RMSEP) of 0.075. Then, these three subsets of feature wavelengths selected by CARS were re-optimised by using successive projections algorithm (SPA). With 7 feature variables from Spectral Set II (3996–600 cm−1) suggested by CARS-SPA, the CARS-SPA-PLSR model predicted tuber CL with R2P of 0.773 and RMSEP of 0.079. Moreover, the CARS-SPA-PLSR model using 5 wavelengths from Spectral Set I exhibited good prediction result, with R2P of 0.913 and RMSEP of 0.058. Although both techniques are capable of determining sweet potato CL in an effective way, the NIR technology demonstrates better predictive capability based on the reduced CARS-SPA-PLSR model.

AB - Near infrared (NIR) hyperspectral imaging and Fourier transform mid infrared (FT-MIR) microspectroscopy were explored in the current study to investigate how constituent elements of sweet potato change during cooking, and in the meantime, to identify sweet potato varieties. Partial least square discriminant analysis (PLSDA) model was established to classify varieties of sweet potato, and the correct classification rate of the PLSDA model using Spectral Set I (964–1645 nm) reached as high as 100%. Competitive adaptive reweighted sampling (CARS) was introduced to choose incipient feature wavelengths from three spectral subsets related to tuber cooking loss (CL). Based on 8 feature variables from Spectral Set I, CARS-SVMR model performed best with the highest coefficient of determination in prediction (R2P) of 0.893 and the lowest root mean square error of prediction (RMSEP) of 0.075. Then, these three subsets of feature wavelengths selected by CARS were re-optimised by using successive projections algorithm (SPA). With 7 feature variables from Spectral Set II (3996–600 cm−1) suggested by CARS-SPA, the CARS-SPA-PLSR model predicted tuber CL with R2P of 0.773 and RMSEP of 0.079. Moreover, the CARS-SPA-PLSR model using 5 wavelengths from Spectral Set I exhibited good prediction result, with R2P of 0.913 and RMSEP of 0.058. Although both techniques are capable of determining sweet potato CL in an effective way, the NIR technology demonstrates better predictive capability based on the reduced CARS-SPA-PLSR model.

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SN - 1537-5110

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