ArticleSensors (Basel, Switzerland)2020
Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 40 citations in OpenAlex.
- Food Fraud in Plant-Based Proteins: Analytical Strategies and Regulatory Perspectives.Foods (Basel, Switzerland) · 2025Review
- Assessment of Peanut Protein Powder Quality by Near-Infrared Spectroscopy and Generalized Regression Neural Network-Based Approach.Foods (Basel, Switzerland) · 2024Article
- Rapid analysis technologies with chemometrics for food authenticity field: A review.Current research in food science · 2024Review
- Proximate Content Monitoring of Black Soldier Fly Larval (Food science of animal resources · 2023Article
- Enhancing Shelf Life Prediction of Fresh Pizza with Regression Models and Low Cost Sensors.Foods (Basel, Switzerland) · 2023Article
- Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics.Current research in food science · 2023Article
- Field Application of a Vis/NIR Hyperspectral Imaging System for Nondestructive Evaluation of Physicochemical Properties in 'Madoka' Peaches.Plants (Basel, Switzerland) · 2022Article
- Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables.Sensors (Basel, Switzerland) · 2022Article
- Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain.Food engineering reviews · 2022Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89-100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R
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Registered trials
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