Infrared Spectroscopy and Hyperspectral Imaging for Detecting Food Contaminants and Residues

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 2350

Special Issue Editors


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Guest Editor
Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Interests: smart sensors; artificial intelligence; food control; food quality; emerging technology

E-Mail Website1 Website2
Guest Editor Assistant
Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Interests: NIR spectroscopy; hyperspectral imaging; machine learning; drying; food safety

Special Issue Information

Dear Colleagues,

Infrared spectroscopy (IR) and hyperspectral imaging (HSI) have gained attention as non-destructive techniques for detecting food contaminants and residues. As food safety continues to be a concern to regulatory agencies and food quality is an increasingly important part of a healthy diet, these advanced technologies offer efficient, real-time monitoring for quality control with a wide application to identify toxic substances such as pesticide residues, heavy metals, and microbial contaminants. In addition to these external contaminants, naturally occurring toxic compounds (e.g., glycoalkaloids, mycotoxins, and plant secondary metabolites) also present significant risks to consumers, and IR/HSI approaches can add to their detection and control.

Different from conventional detection methods, often time-consuming, labor-intensive, and dependent on lab consumables (e.g., chemical reagents), IR and HSI enable in situ, non-invasive analysis. These features enhance their use in day-to-day food safety assessments in different industry segments.

Both technologies are based on light principles to sense and measure their chemical constituents. IR focuses on the absorption of infrared light by specific molecular bonds in food components, commonly used in the near (780–2500 nm) and mid (2500–25,000 nm) ranges, while HSI captures spectral information across a wide range of wavelengths, extracting three-dimensional spatial information. This enhancement allows the detection of subtle changes in food composition and structure, which is the key to tracing contaminants and residues.

Despite the progress their technologies have achieved. There are still challenges in scaling up their application to meet food industry needs. Some include food matrices variability, robust calibration models, and the complexity of spectral data interpretation. Ongoing advancements in sensor design, data fusion, and machine learning have shown promising use to overcome these barriers. With these innovations, IR and HSI are poised to become indispensable tools in the pursuit of safer, higher-quality food systems.

Dr. Marcus Vinicius da Silva Ferreira
Guest Editor

Dr. Md Wadud Ahmed
Guest Editor Assistant

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Keywords

  • infrared spectroscopy (IR)
  • hyperspectral imaging (HSI)
  • food contaminants
  • food safety
  • pesticides
  • heavy metals
  • microbial contamination
  • non-destructive techniques
  • real-time detection
  • data analysis

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Published Papers (3 papers)

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Research

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17 pages, 1178 KB  
Article
A Machine-Learning-Based Prediction Model for Total Glycoalkaloid Accumulation in Yukon Gold Potatoes
by Saipriya Ramalingam, Diksha Singla, Mainak Pal Chowdhury, Michele Konschuh and Chandra Bhan Singh
Foods 2025, 14(19), 3431; https://doi.org/10.3390/foods14193431 - 7 Oct 2025
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Abstract
Potatoes are the most extensively cultivated vegetable crop in Canada and rank as the fifth largest primary agricultural commodity. Given their diverse end uses and significant market value, particularly in processed forms, ensuring consistent quality from harvest to consumption is of critical importance. [...] Read more.
Potatoes are the most extensively cultivated vegetable crop in Canada and rank as the fifth largest primary agricultural commodity. Given their diverse end uses and significant market value, particularly in processed forms, ensuring consistent quality from harvest to consumption is of critical importance. Total glycoalkaloids (TGA) are nitrogen-containing secondary metabolites that are known to accumulate in the tuber as an effect of greening in-field or elsewhere in the supply chain. In this study, 210 Yukon Gold (YG) potatoes were exposed to a constant light source to green over a period of 14 days and sampled in 7-day intervals. The samples were scanned using a short-wave infrared (SWIR) hyperspectral imaging camera in the 900–2500 nm wavelength range. Once individually scanned, pixel-wise spectral data was extracted and averaged for each tuber and matched with its respective ground truth TGA values which were obtained using a High-Performance Liquid Chromatography (HPLC) system. Prediction models using the partial least squares regression technique were developed from the extracted hyperspectral data and reference TGA values. Wavelength selection techniques such as competitive adaptive re-weighted sampling (CARS) and backward elimination (BE) were deployed to reduce the number of contributing wavelengths for practical applications. The best model resulted in a correlation coefficient of cross-validation (R2cv) of 0.72 with a root mean square error of cross-validation (RMSEcv) of 51.50 ppm. Full article
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15 pages, 1128 KB  
Article
Informative Wavelength Selection for Evaluation of Bacterial Spoilage in Raw Salmon (Salmo salar) Fillet Using FT-NIR Spectroscopy
by Roma Panwar, Shin-Ping Lin, Shyh-Hsiang Lin, Jer-An Lin, Yu-Jen Wang and Yung-Kun Chuang
Foods 2025, 14(12), 2074; https://doi.org/10.3390/foods14122074 - 12 Jun 2025
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Abstract
This study highlights the potential of Fourier-transform near-infrared (FT-NIR) spectroscopy for the on-site, nondestructive detection of spoilage caused by bacterial action in raw salmon (Salmo salar) fillets. A stepwise multiple linear regression model with first-derivative spectrum transformation was combined with the [...] Read more.
This study highlights the potential of Fourier-transform near-infrared (FT-NIR) spectroscopy for the on-site, nondestructive detection of spoilage caused by bacterial action in raw salmon (Salmo salar) fillets. A stepwise multiple linear regression model with first-derivative spectrum transformation was combined with the standard normal variate and detrend preprocessing techniques. The model achieved correlation values of 0.97 in both the calibration and validation sample sets, with root mean square error values of 0.18 and 0.20 log CFU/mL, respectively. These accurate results reveal the precision of FT-NIR spectroscopy for assessing the spoilage caused by bacteria. The most informative wavelengths (885.27 nm, 1026.27 nm, 1039.93 nm, 1068.38 nm, 1257.55 nm, 1267.75 nm, and 1453.49 nm) related to the total bacterial count’s identification were obtained. The innovative, cost-effective, and feasible approach outlined in this article is a promising methodology for enhancing the safety and quality standards of various fishery products. Full article
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Review

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29 pages, 2080 KB  
Review
A Comprehensive Review on Minimally Destructive Quality and Safety Assessment of Agri-Food Products: Chemometrics-Coupled Mid-Infrared Spectroscopy
by Lakshmi B. Keithellakpam, Renan Danielski, Chandra B. Singh, Digvir S. Jayas and Chithra Karunakaran
Foods 2025, 14(22), 3805; https://doi.org/10.3390/foods14223805 - 7 Nov 2025
Abstract
Ensuring the quality and safety of agricultural and food products is crucial for protecting consumer health, meeting market expectations, and complying with regulatory requirements. Quality and safety parameters are commonly assessed using chemical and microbiological analyses, which are time-consuming, impractical, and involve the [...] Read more.
Ensuring the quality and safety of agricultural and food products is crucial for protecting consumer health, meeting market expectations, and complying with regulatory requirements. Quality and safety parameters are commonly assessed using chemical and microbiological analyses, which are time-consuming, impractical, and involve the use of toxic solvents, often disrupting the material’s original structure. An alternative technique, infrared spectroscopy, including near-infrared (NIR), mid-infrared (MIR), and short-wave infrared (SWIR), has emerged as a rapid, powerful, and minimally destructive technique for evaluating the quality and safety of food and agricultural products. This review focuses on discussing MIR spectroscopy, particularly Fourier transform infrared (FTIR) techniques, with emphasis on the attenuated total reflectance (ATR) measurement mode (globar infrared light source is commonly used) and on the use of synchrotron radiation (SR) as an alternative high-brightness light source. Both approaches enable the extraction of detailed spectral data related to molecular and functional attributes concerning quality and safety, thereby facilitating the assessment of crop disorders, food chemical composition, microbial contamination (e.g., mycotoxins, bacteria), and the detection of food adulterants, among several other applications. In combination with advanced chemometric techniques, FTIR spectroscopy, whether employing ATR as a measurement mode or SR as a high-brightness light source, is a powerful analytical tool for classification based on attributes, variety, nutritional and geographical origins, with or without minimal sample preparation, no chemical use, and short analysis time. However, limitations exist regarding calibrations, validations, and accessibility. The objective of this review is to address recent technological advancements and existing constraints of FTIR conducted in ATR mode and using SR as a light source (not necessarily in combination). It defines potential pathways for the comprehensive integration of FTIR and chemometrics for real-time quality and safety monitoring systems into the global food supply chain. Full article
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