AIMC Topic: Chemometrics
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Spectroscopic techniques combined with chemometrics for rapid detection of food adulteration: Applications, perspectives, and challenges.
Food research international (Ottawa, Ont.)
Apr 20, 2025
Food adulteration is an important threat to food safety and can be difficult to detect. Some analytical methods are complex and difficult to meet the needs of large numbers of samples. In this study, we introduced the application of six spectroscopic...
Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics.
Food chemistry
Mar 9, 2025
This study investigated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of acrylamide content in potato chips. A total of 300 tubers from two potato varieties (Agria and Jaerla) grown in two seasons and processed und...
Geographical origin differentiation of Philippine Robusta coffee (C. canephora) using X-ray fluorescence-based elemental profiling with chemometrics and machine learning.
Food chemistry
Mar 6, 2025
The increasing demand for authenticity and traceability in high-value crops underscores the need for reliable methods to verify the geographical origin of single-origin coffee and prevent fraud. This study explores a rapid and cost-effective approach...
A review: Integration of NIRS and chemometric methods for tea quality control-principles, spectral preprocessing methods, machine learning algorithms, research progress, and future directions.
Food research international (Ottawa, Ont.)
Feb 15, 2025
With the steady rise in tea production, the need for effective tea quality monitoring has become increasingly pressing. Traditional sensory evaluation and wet chemical detection methods are insufficient for real-time tea quality monitoring. As an eme...
Rapid classification of Camellia seed varieties and non-destructive high-throughput quantitative analysis of fatty acids based on non-targeted fingerprint spectroscopy combined with chemometrics.
Food chemistry
Feb 1, 2025
Camellia oil is a high-quality vegetable oil rich in unsaturated fatty acids (FAs), with quality standardization challenged by the diversity of Camellia seed varieties. This study compared spectroscopy techniques (Near-Infrared [NIR] vs Mid-Infrared ...
Combining stable isotopes and multi-elements with machine learning chemometric models to identify the geographical origins of Tetrastigma hemsleyanum Diels et Gilg.
Food chemistry
Dec 24, 2024
Tetrastigma hemsleyanum Diels et Gilg (T. hemsleyanum) is an edible plant with considerable medicinal properties, the quality of which varies depending on its origin. Therefore economically motivated adulteration has emerged. So there is an urgent ne...
Production monitoring and quality characterization of black garlic using Vis-NIR hyperspectral imaging integrated with chemometrics strategies.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Sep 21, 2024
As a new deep-processing garlic product with notable health benefits, the accurate discrimination of processing stages and prediction of key physicochemical constituents in black garlic are vital for maintaining product quality. This study proposed a...
Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods.
Food chemistry
Sep 16, 2024
Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of tradition...
Chemometrics methods, sensory evaluation and intelligent sensory technologies combined with GAN-based integrated deep-learning framework to discriminate salted goose breeds.
Food chemistry
Aug 22, 2024
The authenticity of salted goose products is concerning for consumers. This study describes an integrated deep-learning framework based on a generative adversarial network and combines it with data from headspace solid phase microextraction/gas chrom...
Spectroscopy-based chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Jun 9, 2024
Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant's nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemical analysis when it is to be done over...
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