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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10214086 | PMC |
| http://dx.doi.org/10.1042/BSR-2021-2681_COR | DOI Listing |
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Feasibility study on fingerprinting organic and conventional mango fruits, chips, and juice using portable near-infrared spectroscopy.
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Department of Food Science and Postharvest Technology, School of Applied Sciences and Technology, Cape Coast Technical University, Cape Coast, Ghana.
This research examined the distinction between organic and conventional mango fruits, chips, and juice using portable near-infrared (NIR) spectroscopy. A comprehensive analysis was conducted on a sample of 100 mangoes (comprising 50 organic and 50 conventional) utilising a portable NIR spectrometer that spans a wavelength range from 900 to 1700 nm. The mangoes were assessed in their entirety and their juice and chip forms.
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Izmir Institute of Technology, Department of Food Engineering, Urla-Izmir, Turkiye. Electronic address:
The detection of adulteration in apple juice concentrate is critical for ensuring product authenticity and consumer safety. This study evaluates the effectiveness of artificial neural networks (ANN) and support vector machines (SVM) in analyzing spectroscopic data to detect adulteration in apple juice concentrate. Four techniques-UV-visible, fluorescence, near-infrared (NIR) spectroscopy, and time domain H nuclear magnetic resonance relaxometry (H NMR)-were used to generate data from both authentic and adulterated apple juice samples.
View Article and Find Full Text PDFMachine learning driven benchtop Vis/NIR spectroscopy for online detection of hybrid citrus quality.
Food Res Int
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State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu Province, China; School of Food Science and Technology, Jiangnan University University, Wuxi, Jiangsu Province, China; Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, China. Electronic address:
The aim of this study was to explore application of visible and near-infrared (Vis/NIR) spectroscopy combined with machine learning models for SSC and TA prediction of hybrid citrus. The Vis/NIR spectra of samples including navel-region, equator-region and multi-region combination spectra in navel-region and equator-region were collected using a benchtop equipment. The performance of SSC and TA prediction models with different region spectra, including partial least squares (PLS), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM) and multilayer feedforward neural network (MFNN), was assessed.
View Article and Find Full Text PDFMean Differences for 3 Delivery Modalities of the Healthy Eating, Staying Active As We Age Curriculum for Older Adults.
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Virginia Cooperative Extension Family Nutrition Program, Virginia Tech, Blacksburg, VA; Department of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA.
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