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Ramirez, Wilmer Ariza; Mishra, Gayatri; Panda, Brajesh Kumar; Jung, Hye-Won; Lee, Sang‐Heon; Lee, Ivan; Singh, Chandra B.

doi:10.1016/j.compag.2022.107078

Cited by 7 publications

(1 citation statement)

References 13 publications

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“…Multispectral imaging (MSI) is an emerging novel technology that efficiently integrates imaging and spectroscopy techniques as an analysis tool for the determination of various food characteristics. MSI works with increased spectral discrimination as compared to conventional methods and provides both spectral and spatial information for each pixel (Fengou et al, 2020; Ramirez et al, 2022). Recently, this non‐destructive and fast technology has been effectively implemented in exposing numerous deceptive and fraudulent adulteration techniques through the detection of several key components.…”

Section: Introductionmentioning

confidence: 99%

Rapid and non‐destructive determination of total phenolic contents using UV–NIR spectroscopy of dehydrated mushroom (Lentinus edodes)

Younas,

Manzoor,

Arqam

et al. 2024

J Food Process Engineering

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Multispectral imaging (MSI) is an emerging technique that ranges from light spectrum of UV–NIR (405–970 nm) used for rapid determination of phenolic contents. The current study focuses on the determination of total phenolic content (TPC) in a fast and non‐invasive way in hot‐air dehydrated Lentinus edodes. The spectral information of MSI has been combined with various chemometrics like partial least squares (PLS), back propagation neural networks (BPNN), and least squares‐support vector machines (LS‐SVM) for the quantitative prediction of phenolic contents. Fresh mushrooms possessed 1.49 and 1.73 GAE g kg−1 TPC in processed samples at 10% moisture content. Comparing models, PLS acquired significant Rp with a lower RMSEP of 0.9980 and 0.1039 and was considered an outstanding algorithm, followed by LS‐SVM of 0.9777 and 0.1417 coefficient of determination and error of prediction data set, respectively. It is concluded that MSI spectroscopy produces highly significant results to determine functional food properties in a rapid and non‐destructive way. The present study will provide a strong platform for the fast online determination of phenolic profile of agricultural commodities.Practical applicationsUtilization of spectroscopy for non‐destructive detection of phenolic contents is defined through the combination of chemometrics with spectra of multispectral imaging system and emphasizes control processing of postharvest horticulture produces. Prediction results R2 above 80% describe that these chemometrics are successfully capable of estimating total phenolic contents in control processing for quality preservation.

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Section: Introductionmentioning

confidence: 99%