New Advances in Food Analysis and Detection

Topic Information

Dear Colleague,

Food quality and safety inspection are important for the food industry and consumers. Analytical techniques, such as mass spectrometry techniques, chromatography techniques, imaging techniques, spectroscopy techniques and sensory techniques, have been widely studied for food analysis and detection. Chemometric methods have also been developed and used alongside the aforementioned analytical techniques. The analysis and detection of food in rapid, accurate and low-cost manners is preferred for food quality and safety inspection. The increasing demand for high-throughput, robust and accurate food analysis as well as detection drives the development of novel analytical techniques and chemometric methods. This topic invites papers that explore the development and application of novel analytical techniques and novel chemometric methods for food analysis and detection. For example, biosensor-, novel-optical-sensor- and deep-learning-based data analysis approaches are encouraged. New applications and the optimization of existing analytical techniques are also welcomed, as is the integration of multiple sensors for food analysis and detection.

Dr. Chu Zhang
Prof. Dr. Pan Gao
Dr. Randy Purves
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400
AppliedChem appliedchem	-	-	2021	18.5 Days	CHF 1000
Chemistry chemistry	2.4	3.2	2019	13.4 Days	CHF 1800
Molecules molecules	4.2	7.4	1996	15.1 Days	CHF 2700

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Published Papers (5 papers)

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20 pages, 8718 KiB  

Open AccessArticle

Apple Surface Defect Detection Based on Gray Level Co-Occurrence Matrix and Retinex Image Enhancement

by Lei Yang, Dexu Mu, Zhen Xu and Kaiyu Huang

Appl. Sci. 2023, 13(22), 12481; https://doi.org/10.3390/app132212481 - 18 Nov 2023

Cited by 1 | Viewed by 1406

Abstract

Aiming at the problems of uneven light reflectivity on the spherical surface and high similarity between the stems/calyxes and scars that exist in the detection of surface defects in apples, this paper proposed a defect detection method based on image segmentation and stem/calyx [...] Read more.

Aiming at the problems of uneven light reflectivity on the spherical surface and high similarity between the stems/calyxes and scars that exist in the detection of surface defects in apples, this paper proposed a defect detection method based on image segmentation and stem/calyx recognition to realize the detection and recognition of surface defects in apples. Preliminary defect segmentation results were obtained by eliminating the interference of light reflection inhomogeneity through adaptive bilateral filtering-based single-scale Retinex (SSR) luminance correction and using adaptive gamma correction to enhance the Retinex reflective layer, and later segmenting the Retinex reflective layer by using a region-growing algorithm. The texture features of apple surface defects under different image processing methods were analyzed based on the gray level co-occurrence matrix, and a support vector machine was introduced for binary classification to differentiate between stems/calyxes and scars. Deploying the proposed defect detection method into the embedded device OpenMV4H7Plus, the accuracy of stem/calyx recognition reached 93.7%, and the accuracy of scar detection reached 94.2%. It has conclusively been shown that the proposed defect detection method can effectively detect apple surface defects in the presence of uneven light reflectivity and stem/calyx interference. Full article

(This article belongs to the Topic New Advances in Food Analysis and Detection)

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14 pages, 3159 KiB  

Open AccessArticle

Determination of Calcium in Meat Products by Automatic Titration with 1,2-Diaminocyclohexane-N,N,N’,N’-tetraacetic Acid

by Alexander Shyichuk, Maria Kowalska, Iryna Shyychuk, Jan Lamkiewicz and Dorota Ziółkowska

Molecules 2023, 28(18), 6592; https://doi.org/10.3390/molecules28186592 - 13 Sep 2023

Cited by 1 | Viewed by 1822

Abstract

Mechanically separated meat (MSM) is a by-product of the poultry industry that requires routine quality assessment. Calcium content is an indirect indicator of bone debris in MSM but is difficult to determine by EDTA titration due to the poor solubility of calcium phosphate. [...] Read more.

Mechanically separated meat (MSM) is a by-product of the poultry industry that requires routine quality assessment. Calcium content is an indirect indicator of bone debris in MSM but is difficult to determine by EDTA titration due to the poor solubility of calcium phosphate. Therefore, 1,2-diaminocyclohexane-N,N,N’,N’-tetraacetic acid was used instead, which has two orders of magnitude higher affinity for calcium ions. In addition, the auxiliary complexing agents triethanolamine and Arsenazo III, an indicator that is sensitive to low calcium concentrations, were used. Automatic titration endpoint detection was performed using an immersion probe at 660 nm. It has been shown that the color change in Arsenazo III can also be read with an RGB camera. The CDTA titration procedure has been tested on commercial Bologna-type sausages and the results were in line with AAS and ICP reference data. The content of calcium in sausages turned out to be very diverse and weakly correlated with the content of MSM. The tested MSM samples had a wide range of calcium content: from 62 to 2833 ppm. Calcium-rich poultry by-products include fat and skin (115 to 412 ppm), articular cartilage (1069 to 1704 ppm), and tendons (532 to 34,539 ppm). The CDTA titration procedure is fully suitable for small meat processing plants due to its simplicity of use and low cost. Full article

(This article belongs to the Topic New Advances in Food Analysis and Detection)

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11 pages, 2782 KiB  

Open AccessArticle

Factors Affecting Incurred Pesticide Extraction in Cereals

by Xiu Yuan, Chang Jo Kim, Won Tae Jeong, Kee Sung Kyung and Hyun Ho Noh

Molecules 2023, 28(15), 5774; https://doi.org/10.3390/molecules28155774 - 31 Jul 2023

Cited by 1 | Viewed by 1238

Abstract

This study investigated the effect of milling on the yields of incurred residues extracted from cereals. Rice, wheat, barley, and oat were soaked in nine pesticides (acetamiprid, azoxystrobin, imidacloprid, ferimzone, etofenprox, tebufenozide, clothianidin, hexaconazole, and indoxacarb), dried, milled, and passed through sieves of [...] Read more.

This study investigated the effect of milling on the yields of incurred residues extracted from cereals. Rice, wheat, barley, and oat were soaked in nine pesticides (acetamiprid, azoxystrobin, imidacloprid, ferimzone, etofenprox, tebufenozide, clothianidin, hexaconazole, and indoxacarb), dried, milled, and passed through sieves of various sizes. The quick, easy, cheap, effective, rugged, and safe method and liquid chromatography–tandem mass spectrometry extracted and quantified the incurred pesticides, respectively. For rice and oat, the yields were higher for vortexed samples than for soaked samples. For rice, the yields improved as the extraction time increased from 1 to 5 min. The optimized method was validated based on the selectivity, limit of quantitation, linearity, accuracy, precision, and the matrix effect. For rice and barley, the average yields improved as the particle size decreased from <10 mesh to >60 mesh. For 40–60-mesh wheat and oat, all pesticides (except tebufenozide in oat) had the highest yields. For cereals, 0.5 min vortexing, 5 min extraction, and >40-mesh particle size should be used to optimize incurred pesticide extraction. Full article

(This article belongs to the Topic New Advances in Food Analysis and Detection)

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15 pages, 3591 KiB  

Open AccessArticle

Kinetics and Distribution of Zearalenone-14-Glucoside and Its Metabolite Zearalenone in Rat, Determined by a Reliable HPLC-MS/MS Method

by Yaling Cai, Zhiqi Zhang, Fang Dong, Zefeng Ma, Kai Fan, Zheng Han, Zhizhong Li and Zhihui Zhao

Appl. Sci. 2023, 13(8), 4990; https://doi.org/10.3390/app13084990 - 16 Apr 2023

Viewed by 1761

Abstract

A reliable high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was established for the simultaneous detection of zearalenone-14-glucoside (ZEN-14G) and its metabolite, zearalenone (ZEN), in the plasma, urine, and various tissues of rats. The performance of the developed method was validated by determining the [...] Read more.

A reliable high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was established for the simultaneous detection of zearalenone-14-glucoside (ZEN-14G) and its metabolite, zearalenone (ZEN), in the plasma, urine, and various tissues of rats. The performance of the developed method was validated by determining the selectivity, linearity (R² > 0.99), sensitivity (lower limit of quantification, 0.1–1 μg/L), recovery (80.7 ± 3.0–112.3 ± 3.1%), precision (0.6–16.5%), and stability (81.7 ± 1.7–104.1 ± 3.9%). Through use of the methodological advances, the subsequent kinetics and distribution after administration of ZEN-14G by gavage were thoroughly investigated. ZEN-14G and ZEN exhibited similar trends in the plasma, and reached their peak concentrations at 10 min and then rapidly decreased. ZEN-14G could be quantified in the stomach, small intestine, and large intestine 24 h after administration, while ZEN was detectable in all tested tissues. Interestingly, ZEN-14G (7.6 ± 3.0 μg/L) and ZEN (977.5 ± 98.0 μg/L) were also detected in the urine 24 h after administration, indicating that ZEN-14G was prone to be slowly and continuously hydrolyzed into ZEN to be absorbed into the plasma and distributed to various tissues, thus leading to a cumulative exposure. Continuous attention should be paid to the co-exposure of ZEN and ZEN-14G, which might pose additional health risks to humans and animals. Full article

(This article belongs to the Topic New Advances in Food Analysis and Detection)

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13 pages, 4431 KiB  

Open AccessArticle

Monitoring of the Dehydration Process of Apple Snacks with Visual Feature Extraction and Image Processing Techniques

by Diana Baigts-Allende, Milena Ramírez-Rodrígues and Roberto Rosas-Romero

Appl. Sci. 2022, 12(21), 11269; https://doi.org/10.3390/app122111269 - 7 Nov 2022

Cited by 5 | Viewed by 2215

Abstract

Monitoring food processing is mandatory for controlling and ensuring product quality. Most of the used techniques are destructive, arduous, and time-consuming. Non-destructive analyses are convenient for rapid and conservative food quality assessment. Color images of apple slices during the manufacturing of healthy snacks [...] Read more.

Monitoring food processing is mandatory for controlling and ensuring product quality. Most of the used techniques are destructive, arduous, and time-consuming. Non-destructive analyses are convenient for rapid and conservative food quality assessment. Color images of apple slices during the manufacturing of healthy snacks were used for monitoring the drying processing. The implementation of the image-based analysis was straightforward, feasible, and low-cost. The parameters analyzed during imagen acquisition for normalizing were: contrast enhancement, binarization, and morphologic processing, varying the illumination and reference between the positions of the camera and object under analysis. Several apple features related to color, texture, and shape were extracted with computer vision techniques and also analyzed. During image analysis, the entropy was one of the most relevant computed features according to principal component analysis, and it was also relevant in terms of physical interpretation. The average percentage of entropy increase was 19.81% in the green and blue channels, while it was 16.82% in the red channel. Other relevant visual features were the skewness and kurtosis in the RGB channels; and textural information such as contrast, correlation, and variance. Full article

(This article belongs to the Topic New Advances in Food Analysis and Detection)

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