Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

Su, Wen-Hao; Xue, Huidan

doi:10.3390/foods10092146

Cited by 31 publications

(20 citation statements)

References 124 publications

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“…As such, MSI has proven useful in a range of bioimaging applications [ 103 ]. Although hyperspectral images can provide more detail about the spectral characteristics of the object being imaged than multispectral images, the acquisition time, complexity and cost of the system are typically quite high [ 104 , 105 , 106 , 107 , 108 , 109 ]. Therefore, MSI using selected characteristic wavelengths is an alternative and more promising approach for the meat industry [ 110 ].…”

Section: Discussionmentioning

confidence: 99%

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review

Fan

2022

Biosensors

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Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry.

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Section: Discussionmentioning

confidence: 99%

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review

Fan

2022

Biosensors

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“…The predictive ability of a developed model is evaluated by the coefficients of determination (R 2 ) and RMSE of the training and testing dataset [ 24 , 32 , 34 ]. The ratio of performance to deviation (RPD) may also be used to further assess the reliability of the predictions [ 34 , 52 , 55 ].…”

Section: Methodsmentioning

confidence: 99%

“…Due to the large amount of data generated through hyperspectral techniques, it is necessary to use appropriate chemometric tools to interpret the information contained in the spectra across the image [ 23 , 24 ]. These often include advanced algorithms for identifying key wavelengths that contribute to the predictive ability of the model, such as competitive adaptive reweighted sampling (CARS) [ 25 ], combined with iterative selection of successive projections algorithm (ISSPA) or a successive projections algorithm (SPA) [ 23 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Rapid Non-Destructive Hyperspectral Imaging Data Model for the Prediction of Pungent Constituents in Dried Ginger

Samrat

Johnson

White

et al. 2022

Foods

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Ginger is best known for its aromatic odour, spicy flavour and health-benefiting properties. Its flavour is derived primarily from two compound classes (gingerols and shogaols), with the overall quality of the product depending on the interaction between these compounds. Consequently, a robust method for determining the ratio of these compounds would be beneficial for quality control purposes. This study investigated the feasibility of using hyperspectral imaging to rapidly determine the ratio of 6-gingerol to 6-shogoal in dried ginger powder. Furthermore, the performance of several pre-processing methods and two multivariate models was explored. The best-performing models used partial least squares regression (PSLR) and least absolute shrinkage and selection operator (LASSO), using multiplicative scatter correction (MSC) and second derivative Savitzky–Golay (2D-SG) pre-processing. Using the full range of wavelengths (~400–1000 nm), the performance was similar for PLSR (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.92) and LASSO models (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.94). These results suggest that hyperspectral imaging combined with chemometric modelling may potentially be used as a rapid, non-destructive method for the prediction of gingerol-to-shogaol ratios in powdered ginger samples.

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“…Recent technological changes have helped researchers in this feld a lot. Computer vision systems (CVSs) are being used for quality control and have recently begun to be used as an objective measurement and evaluation system [6][7][8][9]. CVS technology, which is primarily camera cum computer based, has been considered for sensory characteristics of agricultural products.…”

Section: Introductionmentioning

confidence: 99%

Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm

Krishnan¹,

Aruna

Karthick³

et al. 2023

Scientific Programming

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Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms to classify the seven varieties of dry beans. The balanced dataset was created using the random undersampling method to avoid classification bias of ML algorithms towards the majority group caused by the unbalanced multiclass dataset. The dataset from the UCI ML repository is utilised for developing the multiclass classification model, and the dataset includes the features of seven distinct varieties of dried beans. To address the skewness of the dataset, a Box-Cox transformation (BCT) was performed on the dataset’s attributes. The 22 ML classification algorithms have been applied to the balanced and preprocessed dataset to identify the best ML algorithm. The ML algorithm results have been validated with a 10-fold cross-validation approach, and during validation, the CatBoost ML algorithm achieved the highest overall mean accuracy of 93.8 percent, with a range of 92.05 percent to 95.35 percent.

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Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

Cited by 31 publications

References 124 publications

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review

A Rapid Non-Destructive Hyperspectral Imaging Data Model for the Prediction of Pungent Constituents in Dried Ginger

Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm

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