Origin identification of foxtail millet (Setaria italica) by using green spectral imaging coupled with chemometrics

Wang, Fuxiang; Wang, Chunguang; Song, Shiyong

doi:10.1016/j.infrared.2022.104179

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…However, the geographical origin of soybean with NIR‐HSI has not been reported. Concerning other crops, dimensionality reduction was used to build the principal component analysis–support vector machine (PCA‐SVM) model to distinguish the four regions of Foxtail millet in Inner Mongolia of China (Wang et al., 2022). PLS‐DA, SVM, and random forest were employed to discriminate the Tiegun yam from six producing areas, which showed PLS‐DA obtained the highest accuracy of 98.40% (Zhang et al., 2023).…”

Section: Resultsmentioning

confidence: 99%

Origin traceability and adulteration detection of soybean using near infrared hyperspectral imaging

Li,

Wang,

et al. 2024

Food Frontiers

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Stable isotopes, multi‐elements, metabolic profiles, and integrated spectroscopic fingerprints are priority options for food geographical origin traceability. However, til now, it is still hard to detect adteration with the same one from other geographic origins, which is harder than geographical origin traceability. In this study, partial least square discriminant analysis was employed to build a classification model to discriminate the domestic and imported soybeans after variable selection by uninformative variable elimination using near infrared hyperspectral imaging. As a result, this model could completely discriminate domestic and imported soybeans. Moreover, the developed model was used to detect the adulterated domestic soybean was adulterated with 13.3%, 20.0%, 26.7%, and 33.3% of imported soybean. When the skewness value was less than 0.76 and kurtosis value was less than 1.57 of a sample, the sample was considered as the adulterated. The results indicated that the domestic soybeans adulterated with 20.0%, 26.7%, and 33.3% of imported soybeans were successfully identified. This method could not only identify origin traceability but also detect adulteration of soybeans, which will be beneficial to guarantee the quality and safety of soybean.

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Section: Resultsmentioning

confidence: 99%

Origin traceability and adulteration detection of soybean using near infrared hyperspectral imaging

Li,

Wang,

et al. 2024

Food Frontiers

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“…This study combined hyperspectral imaging with chemometrics to set different rates of sheep manure application and collected 358 samples of foxtail millet flour for the detection of amylose and amylopectin content. The best model for amylose was MSC-RF-IRIV-PLSR, among which the spectral bands of 1134 nm, 1228 nm are in the CH secondary harmonic absorption spectrum, 1355 nm, 1370 nm are in the combined frequency absorption spectrum, and 1478 nm, 1483 nm, 1558 nm, 1582 nm are in the NH primary harmonic absorption spectrum; The best model for amylopectin was MSC-CARS-IRIV-PLSR, among which the spectral band of 983 nm is in the free OH secondary harmonic absorption spectrum, 1007 nm, 1030 nm, 1054 nm, 1068 nm, 1087 nm, 1096 nm are in the combined OH secondary harmonic absorption spectrum, 1143 nm, 1200 nm, 1214 nm are in the CH secondary harmonic absorption spectrum, 1454 nm is in the combined frequency spectrum, and 1619 nm is in the NH primary harmonic absorption spectrum [43]. These selected key bands were all on the chemical groups related to amylose and amylopectin content.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous Detection of Amylose and Amylopectin Content of Foxtail Millet Flour with Hyperspectral Imaging

Wang,

Xue,

Guo

et al. 2023

Preprint

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Foxtail millet, a traditional cereal crop, has gained increasing attention for its high nutritional value and potential health benefits. This study aimed to investigate the simultaneous and rapid detection of amylose and amylopectin content of foxtail millet flour under different sheep manure application rates by hyperspectral imaging combined with chemometrics. The spectral data preprocessing used multiplicative scatter correction (MSC), and the combined algorithm of competitive adaptive reweighted sampling (CARS), random frog (RF), iterated retaining informative variables (IRIV) were employed for key band extraction. The partial least squares regression (PLSR) was then used to establish the prediction model and the regression equation, that was used to visualize the two components. Results demonstrated the key band extraction combined algorithm effectively reduced data dimension without compromising the accuracy of the prediction model. The prediction model for amylose using MSC-RF-IRIV-PLSR exhibited good performance, with the correlation coefficient (R) and root mean square error (RMSE) predicted to be 0.73 and 1.23, respectively. Similarly, the prediction model for amylopectin using MSC-CARS-IRIV-PLSR also demonstrated good performance, with the R and RMSE predicted to be 0.59 and 7.34, respectively. Then, the visualization graph generated clearly shows under the condition of applying 6 m3 of sheep manure, the amount of amylopectin accumulation was highest, and the amount of amylose was lowest. The experimental results offer valuable insights for the rapid detection of nutritional components in foxtail millet, serving as a basis for further research.

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“…Only a limited number of studies were found in the literature for the use of hyperspectral imaging to evaluate small grain seeds such as millet. For example, Wang et al [27] developed an effective and fast technique to identify the origin of foxtail millet seeds using HSI in the 900 to 1700 nm range. They obtained a prediction set accuracy of 95% using the principal component analysis and support vector machine (PCA-SVM) classification model.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging and Machine Learning as a Nondestructive Method for Proso Millet Seed Detection and Classification

Ekramirad,

Doyle,

Loeb

et al. 2024

Foods

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Millet is a small-seeded cereal crop with big potential. There are many different cultivars of proso millet (Panicum miliaceum L.) with different characteristics, bringing forth the issue of sorting which are important for growers, processors, and consumers. Current methods of grain cultivar detection and classification are subjective, destructive, and time-consuming. Therefore, there is a need to develop nondestructive methods for sorting the cultivars of proso millet. In this study, the feasibility of using near-infrared (NIR) hyperspectral imaging (900–1700 nm) to discriminate between different cultivars of proso millet seeds was evaluated. A total of 5000 proso millet seeds were randomly obtained and investigated from the ten most popular cultivars in the United States, namely Cerise, Cope, Earlybird, Huntsman, Minco, Plateau, Rise, Snowbird, Sunrise, and Sunup. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied, and the first two principal components were used as spectral features for building the classification models because they had the largest variance. The classification performance showed prediction accuracy rates as high as 99% for classifying the different cultivars of proso millet using a Gradient tree boosting ensemble machine learning algorithm. Moreover, the classification was successfully performed using only 15 and 5 selected spectral features (wavelengths), with an accuracy of 98.14% and 97.6%, respectively. The overall results indicate that NIR hyperspectral imaging could be used as a rapid and nondestructive method for the classification of proso millet seeds.

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Origin identification of foxtail millet (Setaria italica) by using green spectral imaging coupled with chemometrics

Cited by 8 publications

References 27 publications

Origin traceability and adulteration detection of soybean using near infrared hyperspectral imaging

Origin traceability and adulteration detection of soybean using near infrared hyperspectral imaging

Simultaneous Detection of Amylose and Amylopectin Content of Foxtail Millet Flour with Hyperspectral Imaging

Hyperspectral Imaging and Machine Learning as a Nondestructive Method for Proso Millet Seed Detection and Classification

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