Quantitative prediction of moisture, fat, and protein contents and qualitative discrimination of storage time in yuba: Based on hyperspectral imaging technology

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Prediction of oil content in single maize kernel based on hyperspectral imaging and attention convolution neural network

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New Zealand honey botanical origin classification with hyperspectral imaging

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A novel spectral correction method for eliminating shell interference and improving NIR detection accuracy of internal defect in Torreya grandis nuts

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Rapid detection method of Pleurotus eryngii mycelium based on near infrared spectral characteristics

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Deep learning-based regression of food quality attributes using near-infrared spectroscopy and hyperspectral imaging: A review

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Classification of fritillaria using a portable near-infrared spectrometer and fuzzy generalized singular value decomposition

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Several feature extraction methods combined with near-infrared spectroscopy for identifying the geographical origins of milk

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The qualitative and quantitative analysis of industrial paraffin contamination levels in rice using spectral pretreatment combined with machine learning models

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Rapid and nondestructive identification of rice storage year using hyperspectral technology

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Non-destructive detection of moisture and fatty acid content in rice using hyperspectral imaging and chemometrics

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Simultaneous detection for storage condition and storage time of yellow peach under different storage conditions using hyperspectral imaging with multi-target characteristic selection and multi-task model

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A new quantitative index for the assessment of tomato quality using vis-NIR hyperspectral imaging

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Quality detection of common beans flour using hyperspectral imaging technology: Potential of machine learning and deep learning

10.1016/j.jfca.2025.107424 · doi-reference

Rapid and noninvasive sensory analyses of food products by hyperspectral imaging: Recent application developments

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Classification of fermented cocoa beans (cut test) using computer vision

10.1016/j.jfca.2020.103771 · doi-reference

Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review

10.1016/j.postharvbio.2007.06.024 · doi-reference

A nondestructive detection method for the muti-quality attributes of oats using near-infrared spectroscopy

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Evaluating ripeness in post-harvest stored kiwifruit using VIS-NIR hyperspectral imaging

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Quantitative inversion model of protein and fat content in milk based on hyperspectral techniques

10.1016/j.idairyj.2022.105467 · doi-reference

Classification of maize seeds of different years based on hyperspectral imaging and model updating

10.1016/j.compag.2016.01.029 · doi-reference

Simultaneous determination of rice adulteration with aged rice and paraffin contaminated rice by hyperspectral imaging

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Detection of the amylose and amylopectin contents of rice by hyperspectral imaging combined with a CNN-AdaBoost model

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Rapid determination of reducing sugar content in sweet potatoes using NIR spectra

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Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [ipomoea batatas (L.) lam] by NIR hyperspectral imaging

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Maturity classification of rapeseed using hyperspectral image combined with machine learning

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Comparison of near-infrared and Mid-Infrared spectroscopy for the identification and quantification of argan oil adulteration through PCA, PLS-DA and PLS

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Using near-infrared spectroscopy to determine moisture content, gel strength, and viscosity of gelatin

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Identification of unfertilized duck eggs before hatching using visible/near infrared transmittance spectroscopy

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10.1016/j.chemolab.2008.07.010

10.1016/j.chemolab.2008.07.010 · doi-reference

Classification of tea quality levels using near-infrared spectroscopy based on CLPSO-SVM

10.3390/foods11111658 · doi-reference

Non-destructive quality classification of rice taste properties based on near-infrared spectroscopy and machine learning algorithms

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Hyperspectral imaging combined with convolutional neural network for pu’er ripe tea origin recognition

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Dynamic patterns of quality deterioration, oxidative stability, and flavor evolution in yuba during long-term storage

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Near-infrared (NIR) spectroscopy for motor oil classification: From discriminant analysis to support vector machines

10.1016/j.microc.2010.12.007 · doi-reference

A non-destructive determination of peroxide values, total nitrogen and mineral nutrients in an edible tree nut using hyperspectral imaging

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Detection of defective cocoa beans using machine learning techniques and NIR spectral data fusion

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