A study of starch content detection and the visualization of fresh-cut potato based on hyperspectral imaging

Wang, Fuxiang; Wang, Chunguang; Song, Shiyong

doi:10.1039/d1ra01013a

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…Determination coefficients of 0.84 and 0.77 were obtained in the prediction test for 5 color indicators and the water content, respectively. In a more recent study, Wang et al ( 36 ) predicted the starch content of potatoes and visualized its distribution in fresh-cut slices by HSI. A system covering the 380–1,000 nm spectral range was used to acquire the images of 96 potato slices of 0.2 cm.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude

López-Maestresalas

López-Molina²,

Oliva-Lobo³

et al. 2022

Front. Nutr.

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The potato (Solanum tuberosum L.) is the world’s fifth most important staple food with high socioeconomic relevance. Several potato cultivars obtained by selection and crossbreeding are currently on the market. This diversity causes tubers to exhibit different behaviors depending on the processing to which they are subjected. Therefore, it is interesting to identify cultivars with specific characteristics that best suit consumer preferences. In this work, we present a method to classify potatoes according to their cooking or frying as crisps aptitude using NIR hyperspectral imaging (HIS) combined with a Partial Least Squares Discriminant Analysis (PLS-DA). Two classification approaches were used in this study. First, a classification model using the mean spectra of a dataset composed of 80 tubers belonging to 10 different cultivars. Then, a pixel-wise classification using all the pixels of each sample of a small subset of samples comprised of 30 tubers. Hyperspectral images were acquired using fresh-cut potato slices as sample material placed on a mobile platform of a hyperspectral system in the NIR range from 900 to 1,700 nm. After image processing, PLS-DA models were built using different pre-processing combinations. Excellent accuracy rates were obtained for the models developed using the mean spectra of all samples with 90% of tubers correctly classified in the external dataset. Pixel-wise classification models achieved lower accuracy rates between 66.62 and 71.97% in the external validation datasets. Moreover, a forward interval PLS (iPLS) method was used to build pixel-wise PLS-DA models reaching accuracies above 80 and 71% in cross-validation and external validation datasets, respectively. Best classification result was obtained using a subset of 100 wavelengths (20 intervals) with 71.86% of pixels correctly classified in the validation dataset. Classification maps were generated showing that false negative pixels were mainly located at the edges of the fresh-cut slices while false positive were principally distributed at the central pith, which has singular characteristics.

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Section: Resultsmentioning

confidence: 99%

Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude

López-Maestresalas

López-Molina²,

Oliva-Lobo³

et al. 2022

Front. Nutr.

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“…Color, water, and starch content are quality indicators of fresh-cut potatoes [49,52]. Also, in this case, Vis/NIR HSI systems were demonstrated to be effective tools to build prediction maps (Figure 10) for the distribution of such qualitative indicators using different modeling approaches (PLS, SVM, and LS-SVM).…”

Section: Potatoesmentioning

confidence: 89%

“…Also, in this case, Vis/NIR HSI systems were demonstrated to be effective tools to build prediction maps (Figure 10) for the distribution of such qualitative indicators using different modeling approaches (PLS, SVM, and LS-SVM). Indeed, for color and water content prediction, an R 2 about 0.8 was obtained using any of the modeling methods [49], while Wang et al (2021) extracted a bulk of characteristic wavelengths (about 10 to 30 wavelengths according to the applied selection method) to build a PLS model for the starch content prediction (R 2 = 0.95 and RMSEP of 1.63 g kg −1 ) [52].…”

Section: Potatoesmentioning

confidence: 99%

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Hyperspectral Imaging for Fresh-Cut Fruit and Vegetable Quality Assessment: Basic Concepts and Applications

Vignati,

Tugnolo,

Giovenzana

et al. 2023

Applied Sciences

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During the last two decades, hyperspectral imaging (HSI) has been one of the most studied and applied techniques in the field of nondestructive monitoring systems for the fruit and vegetable supply chain. This review provides HSI technical aspects (i.e., device features) and data analysis approaches (i.e., data processing and qualitative/quantitative modeling) for fresh-cut products, focusing on the different applications which the literature offers and the possible scale-up for process monitoring. Moreover, new frontiers in the development of possible process analytical technologies of cost-effective and hand-held HSI devices are presented and discussed. Even though the performance of these new proximal sensing tools needs to be carefully evaluated, new applicative research perspectives in the development of a proximal sensing approach based on HSI sensor networks are ready to be studied and developed for finding field applications (i.e., precision agriculture, food processing, and more) and enabling faster and more convenient analysis while maintaining the accuracy and capabilities of traditional HSI systems.

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“…Wang et al. (2021) effectively mapped the spatial distribution of starch content in fresh‐cut potatoes by using the VNIR‐HSI system at 382.23‐1004.78 nm. In this study, the optimal prediction model of multiple scattering correction (MSC)‐CARS‐PLSR ( R p = 0.9467) was determined, and pseudo‐color technology was introduced into the constructed model to determine the starch content, and the starch content distribution map was automatically generated.…”

Section: Application Of Various Imaging Technologies In Fresh‐cutting...mentioning

confidence: 99%

Intelligent detection for fresh‐cut fruit and vegetable processing: Imaging technology

Tang

Zhang

Mujumdar

2022

Comp Rev Food Sci Food Safe

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Fresh-cut fruits and vegetables are healthy and convenient ready-to-eat foods, and the final quality is related to the raw materials and each step of the cutting unit. It is necessary to integrate suitable intelligent detection technologies into the production chain so as to inspect each operation to ensure high product quality. In this paper, several imaging technologies that can be applied online to the processing of fresh-cut products are reviewed, including: multispectral/hyperspectral imaging (M/HSI), fluorescence imaging (FI), X-ray imaging (XRI), ultrasonic imaging, thermal imaging (TI), magnetic resonance imaging (MRI), terahertz imaging, and microwave imaging (MWI). The principles, advantages, and limitations of these imaging technologies are critically summarized. The potential applications of these technologies in online quality control and detection during the fresh-cut processing are comprehensively discussed, including quality of raw materials, contamination of cutting equipment, foreign bodies mixed in the processing, browning and microorganisms of the cutting surface, quality/shelf-life evaluation, and so on. Finally, the challenges and future application prospects of imaging technology in industrialization are presented.

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A study of starch content detection and the visualization of fresh-cut potato based on hyperspectral imaging

Abstract: The starch content of fresh-cut potato chips was detected via hyperspectral imaging, and a representation of the visual distribution of the starch content was realized.

Cited by 10 publications

References 37 publications

Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude

Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude

Hyperspectral Imaging for Fresh-Cut Fruit and Vegetable Quality Assessment: Basic Concepts and Applications

Intelligent detection for fresh‐cut fruit and vegetable processing: Imaging technology

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