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Mycotoxins are secondary metabolites of certain moulds, prevalent in 60–80% of food crops and many processed products but challenging to eliminate. Consuming mycotoxin-contaminated food and feed can lead to various adverse effects on humans and livestock. Therefore, testing mycotoxin residue levels is critical to ensure food safety. Gold standard analytical methods rely on liquid chromatography coupled with optical detectors or mass spectrometers, which are high-cost with limited capacity. This study reported the successful development of a microfluidic "lab-on-a-chip" device to enrich and detect zearalenone in food samples based on the fluorescence quenching effect of quantum dots and selective affinity of molecularly imprinted polymers (MIPs). The dummy template and functional polymer were synthesized and characterized, and the detailed microfluidic chip design and optimization of the flow conditions in the enrichment module were discussed. The device achieved an enrichment factor of 9.6 (±0.5) in 10 min to quantify zearalenone spiked in food with high recoveries (91–105%) at 1–10 mg kg−1, covering the concerned residue levels in the regulations. Each sample-to-answer test took only 20 min, involving 3 min of manual operation and no advanced equipment. This microfluidic device was mostly reusable, with a replaceable detection module compatible with fluorescence measurement using a handheld fluorometer. To our best knowledge, the reported device was the first application of an MIP-based microfluidic sensor for detecting mycotoxin in real food samples, providing a novel, rapid, portable, and cost-effective tool for monitoring mycotoxin contamination for food safety and security. [ABSTRACT FROM AUTHOR]

Food safety remains one of the most important issues in most countries and the detection of food hazards plays a key role in the systematic approach to ensuring food safety. Rapid, easy-to-use and low-cost analytical tools are required to detect chemical hazards in foods. As a promising candidate, microfluidic paper-based analytical devices (μPADs) have been rarely applied to real food samples for testing chemical hazards, although numerous papers have been published in this field in the last decade. This review discusses the current status and concerns of the μPAD applications in the detection of chemical hazards in foods from the perspective of food scientists, mainly for an audience with a background in mechanical and chemical engineering who may have interests in exploring the potential of μPAD to address real-world food safety issues. [ABSTRACT FROM AUTHOR]

Total phenolic content (TPC) and antioxidant capacity of maple syrup were determined using Raman spectroscopy and deep learning. TPC was determined by Folin-Ciocalteu assay, while the antioxidant capacity was measured by 2,2-diphenyl-1picrylhydrazyl (DPPH) assay, oxygen radical absorbance capacity (ORAC) assay, and ferric reducing antioxidant power (FRAP) assay. A total of 360 spectra were collected from 36 maple syrup samples of different colours (dark, amber, light) by both benchtop and portable Raman spectrometers. These spectra were used to establish predictive models for assessing the antioxidant profiles of maple syrup. Deep learning models developed along with portable Raman spectroscopy exhibited comparable predictive performance to those developed along with benchtop Raman spectroscopy. Base on the spectral dataset collected using portable Raman spectroscopy, the developed deep learning models exhibited low RMSEs (root mean square errors, 7.2-17.9 % of mean reference values), low MAEs (mean absolute errors, 5.2-13.1 % of mean reference values) and high R 2 values (>0.88). The results showed a great goodness of fit and accuracy for predicting the antioxidant profiles of maple syrup, indicating the potential of using portable Raman spectrometer for on-site analysis of antioxidant profiles of maple syrup., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The total phenolic content (TPC) and antioxidant capacity (TAC) of haskap berries cultivated in various locations across Alberta were analyzed using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. The Folin-Ciocalteu assay was used to determine TPC, while TAC was quantified by 2,2-diphenyl-1-picrylhydrazl radicals (DPPH) assay and oxygen radical absorbance capacity (ORAC) assay. Three tenfold cross-validated partial least-squares regression (PLSR) models and three fivefold cross-validated deep learning models were developed separately based on FT-IR spectra collected from 22 haskap berry samples and their corresponding reference values determined through Folin-Ciocalteu, DPPH, and ORAC assays. The deep learning models (R 2  = 0.95, 0.93, and 0.90 for Folin-Ciocalteu, DPPH, and ORAC assays, respectively) demonstrated better prediction capability compared to the PLSR models (R 2  = 0.74, 0.72, and 0.66 for Folin-Ciocalteu, DPPH, and ORAC assays, respectively). In addition, PLS loading plots indicated that phenolic contents and polysaccharides in haskap berries could contribute to their antioxidant capacity. Using ATR-FTIR to estimate the TPC and TAC of fruits offers a rapid alternative to the conventional chemical assays., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Determination of pesticide residues remains a challenge in traditional Chinese medicines in which complex compounds may interfere with analysis signals. This study reports the development of a simple, effective, and high-throughput method combining gas chromatography-tandem mass spectrometry (GC-MS/MS) with either QuEChERS or solid phase extraction (SPE) to determine 147 pesticide residues in traditional Chinese medicines simultaneously. In SPE, the mixture of n -hexane and ethyl acetate (1:1, v/v) was selected to extract 147 pesticides in honeysuckle, and the extracted pesticides were determined by GC-MS/MS. The limits of detection for all pesticides were within 0.01-0.05 mg/kg. The recoveries were within 70-120% and the relative standard deviations were below 20% for over 90% pesticides. The coefficients of determination were up to 0.999 for the linearity between MS signals and different concentrations of pesticides (20-200 ng/mL). The analytical performance was confirmed in determining pesticide residues in dried tangerine peel. SPE achieved comparable recoveries for all pesticides compared to the QuEChERS method., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)