What does this research mean for the field?

Near-infrared (NIR) spectroscopy, when coupled with chemometric models, provides a rapid, non-destructive, and highly accurate method for detecting trace levels of adulterants and contaminants across various food matrices. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Summarize the instrumentation, chemometric data analysis, and applications of near-infrared spectroscopy for identifying adulterants and contaminants in diverse food products.

September 2, 2026MoleculesOpen Access

Applications of NIR Spectroscopy and Chemometrics for Food Authentication and Safety: Detection of Adulterants and Contaminants

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Authors

VPVanessa PellicorioUniversity of Pavia RCRaffaella ColomboUniversity of Pavia APAdele PapettiUniversity of Pavia

Discussion

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Overview

Review demonstrates high accuracy of near-infrared spectroscopy coupled with chemometrics in detecting trace food adulterants, highlighting viable non-destructive screening across commercial products.

Key Points

Summarize the instrumentation, chemometric data analysis, and applications of near-infrared spectroscopy for identifying adulterants and contaminants in diverse food products.
Synthesized principles and instrumentation of near-infrared (NIR) spectroscopy, including benchtop systems and portable devices, for solvent-free, non-destructive food quality control.
Evaluated chemometric approaches—such as partial least squares regression (PLSR), support vector machines (SVM), random forest (RF), and convolutional neural networks (CNNs)—applied across matrices including oils, milk, juices, spices, cereals, and dietary supplements.
NIR spectroscopy coupled with chemometrics reliably detected and quantified trace adulterants and contaminants down to parts-per-million (ppm) concentrations.
Machine learning models (PLSR, SVM, RF, CNNs) achieved high sensitivity and classification accuracy across both liquid and solid commercial food matrices.

Cite This Study

Pellicorio et al. (2026) studied this question.

synapsesocial.com/papers/6a97e25ec562ede874ec67d2 https://doi.org/10.3390/molecules31173067

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September 2, 2026MoleculesOpen Access

Applications of NIR Spectroscopy and Chemometrics for Food Authentication and Safety: Detection of Adulterants and Contaminants

View Full Paper

Ask AI

Bookmark

Authors

VPVanessa PellicorioUniversity of Pavia RCRaffaella ColomboUniversity of Pavia APAdele PapettiUniversity of Pavia

Discussion

Member takes

Overview

Key Points

Summarize the instrumentation, chemometric data analysis, and applications of near-infrared spectroscopy for identifying adulterants and contaminants in diverse food products.
Synthesized principles and instrumentation of near-infrared (NIR) spectroscopy, including benchtop systems and portable devices, for solvent-free, non-destructive food quality control.
Evaluated chemometric approaches—such as partial least squares regression (PLSR), support vector machines (SVM), random forest (RF), and convolutional neural networks (CNNs)—applied across matrices including oils, milk, juices, spices, cereals, and dietary supplements.
NIR spectroscopy coupled with chemometrics reliably detected and quantified trace adulterants and contaminants down to parts-per-million (ppm) concentrations.
Machine learning models (PLSR, SVM, RF, CNNs) achieved high sensitivity and classification accuracy across both liquid and solid commercial food matrices.

Cite This Study

Pellicorio et al. (2026) studied this question.

synapsesocial.com/papers/6a97e25ec562ede874ec67d2 https://doi.org/10.3390/molecules31173067

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