Foods

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11 pages, 438 KiB

Open AccessArticle

Rapid Classification of Milk Using a Cost-Effective Near Infrared Spectroscopy Device and Variable Cluster–Support Vector Machine (VC-SVM) Hybrid Models

by Eleonora Buoio, Valentina Colombo, Elena Ighina and Francesco Tangorra

Foods 2024, 13(20), 3279; https://doi.org/10.3390/foods13203279 - 16 Oct 2024

Viewed by 837

Abstract

Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly [...] Read more.

Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly used technique for distinguishing pure milk from adulterated milk, even when it comes from different animal species. More recently, portable spectrometers have enabled in situ analysis with analytical performance comparable to that of benchtop instruments. Partial Least Square (PLS) analysis is the most popular tool for developing calibration models, although the increasing availability of portable near infrared spectroscopy (NIRS) has led to the use of alternative supervised techniques, including support vector machine (SVM). The aim of this study was to develop and implement a method based on the combination of a compact and low-cost Fourier Transform near infrared (FT-NIR) spectrometer and variable cluster–support vector machine (VC-SVM) hybrid model for the rapid classification of milk in accordance with EU Regulation EC No. 1308/2013 without any pre-treatment. The results obtained from the external validation of the VC-SVM hybrid model showed a perfect classification capacity (100% sensitivity, 100% specificity, MCC = 1) for the radial basis function (RBF) kernel when used to classify whole vs. not-whole and skimmed vs. not-skimmed milk samples. A strong classification capacity (94.4% sensitivity, 100% specificity, MCC = 0.95) was also achieved in discriminating semi-skimmed vs. not-semi-skimmed milk samples. This approach provides the dairy industry with a practical, simple and efficient solution to quickly identify skimmed, semi-skimmed and whole milk and detect potential fraud. Full article

(This article belongs to the Special Issue Applications of Spectroscopy Combined Machine Learning in Food Quality and Safety)

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12 pages, 2861 KiB

Open AccessArticle

Deep Learning-Based Automated Cell Detection-Facilitated Meat Quality Evaluation

by Hui Zheng, Nan Zhao, Saifei Xu, Jin He, Ricardo Ospina, Zhengjun Qiu and Yufei Liu

Foods 2024, 13(14), 2270; https://doi.org/10.3390/foods13142270 - 18 Jul 2024

Viewed by 878

Abstract

Meat consumption is increasing globally. The safety and quality of meat are considered important issues for human health. During evaluations of meat quality and freshness, microbiological parameters are often analyzed. Counts of indicator cells can provide important references for meat quality. In order [...] Read more.

Meat consumption is increasing globally. The safety and quality of meat are considered important issues for human health. During evaluations of meat quality and freshness, microbiological parameters are often analyzed. Counts of indicator cells can provide important references for meat quality. In order to eliminate the error of manual operation and improve detection efficiency, this paper proposed a Convolutional Neural Network (CNN) with a backbone called Detect-Cells-Rapidly-Net (DCRNet), which can identify and count stained cells automatically. The DCRNet replaces the single channel of residual blocks with the aggregated residual blocks to learn more features with fewer parameters. The DCRNet combines the deformable convolution network to fit flexible shapes of stained animal cells. The proposed CNN with DCRNet is self-adaptive to different resolutions of images. The experimental results indicate that the proposed CNN with DCRNet achieves an Average Precision of 81.2% and is better than traditional neural networks for this task. The difference between the results of the proposed method and manual counting is less than 0.5% of the total number of cells. The results indicate that DCRNet is a promising solution for cell detection and can be equipped in future meat quality monitoring systems. Full article

(This article belongs to the Special Issue Applications of Spectroscopy Combined Machine Learning in Food Quality and Safety)

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16 pages, 3196 KiB

Open AccessArticle

Deoxynivalenol Detection beyond the Limit in Wheat Flour Based on the Fluorescence Hyperspectral Imaging Technique

by Chengzhi Wang, Xiaping Fu, Ying Zhou and Feng Fu

Foods 2024, 13(6), 897; https://doi.org/10.3390/foods13060897 - 15 Mar 2024

Cited by 1 | Viewed by 1357

Abstract

Deoxynivalenol (DON) is a harmful fungal toxin, and its contamination in wheat flour poses a food safety concern globally. This study proposes the combination of fluorescence hyperspectral imaging (FHSI) and qualitative discrimination methods for the detection of excessive DON content in wheat flour. [...] Read more.

Deoxynivalenol (DON) is a harmful fungal toxin, and its contamination in wheat flour poses a food safety concern globally. This study proposes the combination of fluorescence hyperspectral imaging (FHSI) and qualitative discrimination methods for the detection of excessive DON content in wheat flour. Wheat flour samples were prepared with varying DON concentrations through the addition of trace amounts of DON using the wet mixing method for fluorescence hyperspectral image collection. SG smoothing and normalization algorithms were applied for original spectra preprocessing. Feature band selection was carried out by applying the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the random frog algorithm on the fluorescence spectrum. Random forest (RF) and support vector machine (SVM) classification models were utilized to identify wheat flour samples with DON concentrations higher than 1 mg/kg. The results indicate that the SG–CARS–RF and SG–CARS–SVM models showed better performance than other models, achieving the highest recall rate of 98.95% and the highest accuracy of 97.78%, respectively. Additionally, the ROC curves demonstrated higher robustness on the RF algorithm. Deep learning algorithms were also applied to identify the samples that exceeded safety standards, and the convolutional neural network (CNN) model achieved a recognition accuracy rate of 97.78% for the test set. In conclusion, this study demonstrates the feasibility and potential of the FHSI technique in detecting DON infection in wheat flour. Full article

(This article belongs to the Special Issue Applications of Spectroscopy Combined Machine Learning in Food Quality and Safety)

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15 pages, 3956 KiB

Open AccessArticle

Multi-Spectral Food Classification and Caloric Estimation Using Predicted Images

by Ki-Seung Lee

Foods 2024, 13(4), 551; https://doi.org/10.3390/foods13040551 - 11 Feb 2024

Viewed by 1597

Abstract

In nutrition science, methods that accomplish continuous recognition of ingested foods with minimal user intervention have great utility. Our recent study showed that using images taken at a variety of wavelengths, including ultraviolet (UV) and near-infrared (NIR) bands, improves the accuracy of food [...] Read more.

In nutrition science, methods that accomplish continuous recognition of ingested foods with minimal user intervention have great utility. Our recent study showed that using images taken at a variety of wavelengths, including ultraviolet (UV) and near-infrared (NIR) bands, improves the accuracy of food classification and caloric estimation. With this approach, however, analysis time increases as the number of wavelengths increases, and there are practical implementation issues associated with a large number of light sources. To alleviate these problems, we proposed a method that used only standard red-green-blue (RGB) images to achieve performance that approximates the use of multi-wavelength images. This method used RGB images to predict the images at each wavelength (including UV and NIR bands), instead of using the images actually acquired with a camera. Deep neural networks (DNN) were used to predict the images at each wavelength from the RGB images. To validate the effectiveness of the proposed method, feasibility tests were carried out on 101 foods. The experimental results showed maximum recognition rates of 99.45 and 98.24% using the actual and predicted images, respectively. Those rates were significantly higher than using only the RGB images, which returned a recognition rate of only 86.3%. For caloric estimation, the minimum values for mean absolute percentage error (MAPE) were 11.67 and 12.13 when using the actual and predicted images, respectively. These results confirmed that the use of RGB images alone achieves performance that is similar to multi-wavelength imaging techniques. Full article

(This article belongs to the Special Issue Applications of Spectroscopy Combined Machine Learning in Food Quality and Safety)

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16 pages, 1736 KiB

Open AccessArticle

Utilizing VIS-NIR Technology to Generate a Quality Index (Q_i) Model of Barhi Date Fruits at the Khalal Stage Stored in a Controlled Environment

by Abdullah M. Alhamdan

Foods 2024, 13(2), 345; https://doi.org/10.3390/foods13020345 - 22 Jan 2024

Cited by 3 | Viewed by 1310

Abstract

Saudi Arabia is a prominent producer of dates, producing 1.6 million tons annually. There is a need to evaluate the physical properties and quality of fruits non-destructively and then modeled and predict them throughout the storage period. The aim of the current study [...] Read more.

Saudi Arabia is a prominent producer of dates, producing 1.6 million tons annually. There is a need to evaluate the physical properties and quality of fruits non-destructively and then modeled and predict them throughout the storage period. The aim of the current study was to generate a quality index (Q_i) and visible–near-infrared spectra (VIS-NIR) models non-destructively to predict properties of Barhi dates including objective and sensory evaluations. A total of 1000 Barhi fruits were sorted into three stages of maturation, ranging from 80 to 100% yellowish. The physical properties (hardness, color, TSS, pH, and sensory evaluations) of Barhi dates were measured and modeled with Q_i based on VIS-NIR of fresh Barhi fruits and during storage in ambient (25 °C), cold (1 °C), and CA (1 °C with 5%:5% O₂:CO₂, 85% RH) conditions for up to 3 months. The prediction of Q_i was non-destructively based on VIS-NIR utilizing PLSR and ANN data analysis. The results showed that the shelf-life of stored Barhi fruits were 20, 40, and 120 days corresponding to 25 °C, cold (1 °C), and CA, respectively. It was found that VIS-NIR spectroscopy was helpful in estimating the Q_i of Barhi fruits for PLSR and ANN data analysis, respectively, in calibration with an R² of 0.793 and 0.912 and RMSEC of 0.110 and 0.308 and cross-validation with an R² of 0.783 and 0.912 and RMSEC of 0.298 and 0.308. The VIS-NIR spectrum has proven to be an effective method for the evaluation of the Q_i of Barhi fruits and their physical properties throughout the supply chain in the handling, processing, transportation, storage and retail sectors. It was found that ANN is more suitable than PLSR analysis. Full article

(This article belongs to the Special Issue Applications of Spectroscopy Combined Machine Learning in Food Quality and Safety)

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15 pages, 15835 KiB

Open AccessArticle

Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging

by Jong-Jin Park, Jeong-Seok Cho, Gyuseok Lee, Dae-Yong Yun, Seul-Ki Park, Kee-Jai Park and Jeong-Ho Lim

Foods 2023, 12(18), 3471; https://doi.org/10.3390/foods12183471 - 18 Sep 2023

Cited by 2 | Viewed by 1817

Abstract

This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), [...] Read more.

This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), or 0:100 (P0), and Allura Red was added to the red pepper pericarp/seed mixture at 0.05% (A), 0.1% (B), and 0.15% (C). The results of principal component analysis (PCA) using the L, a, and b values; hue angle; and chroma showed that the pure pericarp powder (P100) was not easily distinguished from some adulterated samples (P50A-C, P75A-C, and P100B,C). Adulterated red pepper powder was detected by applying machine learning techniques, including linear discriminant analysis (LDA), linear support vector machine (LSVM), and k-nearest neighbor (KNN), based on spectra obtained from SWIR (1,000–1,700 nm). Linear discriminant analysis determined adulteration with 100% accuracy when the samples were divided into four categories (acceptable, adulterated by Allura Red, adulterated by seeds, and adulterated by seeds and Allura Red). The application of SWIR technology and machine learning detects adulteration with Allura Red and seeds in red pepper powder. Full article

(This article belongs to the Special Issue Applications of Spectroscopy Combined Machine Learning in Food Quality and Safety)

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14 pages, 2201 KiB

Open AccessArticle

Quantification and Detection of Ground Garlic Adulteration Using Fourier-Transform Near-Infrared Reflectance Spectra

by Michal Daszykowski, Michal Kula and Ivana Stanimirova

Foods 2023, 12(18), 3377; https://doi.org/10.3390/foods12183377 - 8 Sep 2023

Cited by 1 | Viewed by 1396

Abstract

This study demonstrates the rapid and cost-effective possibility of quantifying adulterant amounts (corn flour or corn starch) in ground and dried garlic samples. Prepared mixtures with different concentrations of selected adulterant were effectively characterized using Fourier-transform near-infrared reflectance spectra (FT-NIR), and multivariate calibration [...] Read more.

This study demonstrates the rapid and cost-effective possibility of quantifying adulterant amounts (corn flour or corn starch) in ground and dried garlic samples. Prepared mixtures with different concentrations of selected adulterant were effectively characterized using Fourier-transform near-infrared reflectance spectra (FT-NIR), and multivariate calibration models were developed using two methods: principal component regression (PCR) and partial least squares regression (PLSR). They were constructed for optimally preprocessed FT-NIR spectra, and PLSR models generally performed better regarding model fit and predictions than PCR. The optimal PLSR model, built to estimate the amount of corn flour present in the ground and dried garlic samples, was constructed for the first derivative spectra obtained after Savitzky–Golay smoothing (fifteen sampling points and polynomial of the second degree). It demonstrated root mean squared errors for calibration and validation samples equal to 1.8841 and 1.8844 (i.e., 1.88% concerning the calibration range), respectively, and coefficients of determination equal to 0.9955 and 0.9858. The optimal PLSR model constructed for spectra after inverse scattering correction to assess the amount of corn starch had root mean squared errors for calibration and validation samples equal to 1.7679 and 1.7812 (i.e., 1.77% and 1.78% concerning the calibration range), respectively, and coefficients of determination equal to 0.9961 and 0.9873. It was also possible to discriminate samples adulterated with corn flour or corn starch using partial least squares discriminant analysis (PLS-DA). The optimal PLS-DA model had a very high correct classification rate (99.66%), sensitivity (99.96%), and specificity (99.36%), calculated for external validation samples. Uncertainties of these figures of merit, estimated using the Monte Carlo validation approach, were relatively small. One-class classification partial least squares models, developed to detect the adulterant type, presented very optimistic sensitivity for validation samples (above 99%) but low specificity (64% and 45.33% for models recognizing corn flour or corn starch adulterants, respectively). Through experimental investigation, chemometric data analysis, and modeling, we have verified that the FT-NIR technique exhibits the required sensitivity to quantify adulteration in dried ground garlic, whether it involves corn flour or corn starch. Full article

(This article belongs to the Special Issue Applications of Spectroscopy Combined Machine Learning in Food Quality and Safety)

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20 pages, 2486 KiB

Open AccessArticle

Multispectral Food Classification and Caloric Estimation Using Convolutional Neural Networks

by Ki-Seung Lee

Foods 2023, 12(17), 3212; https://doi.org/10.3390/foods12173212 - 25 Aug 2023

Cited by 5 | Viewed by 1552

Abstract

Continuous monitoring and recording of the type and caloric content of ingested foods with a minimum of user intervention is very useful in preventing metabolic diseases and obesity. In this paper, automatic recognition of food type and caloric content was achieved via the [...] Read more.

Continuous monitoring and recording of the type and caloric content of ingested foods with a minimum of user intervention is very useful in preventing metabolic diseases and obesity. In this paper, automatic recognition of food type and caloric content was achieved via the use of multi-spectral images. A method of fusing the RGB image and the images captured at ultra violet, visible, and near-infrared regions at center wavelengths of 385, 405, 430, 470, 490, 510, 560, 590, 625, 645, 660, 810, 850, 870, 890, 910, 950, 970, and 1020 nm was adopted to improve the accuracy. A convolutional neural network (CNN) was adopted to classify food items and estimate the caloric amounts. The CNN was trained using 10,909 images acquired from 101 types. The objective functions including classification accuracy and mean absolute percentage error (MAPE) were investigated according to wavelength numbers. The optimal combinations of wavelengths (including/excluding the RGB image) were determined by using a piecewise selection method. Validation tests were carried out on 3636 images of the food types that were used in training the CNN. As a result of the experiments, the accuracy of food classification was increased from 88.9 to 97.1% and MAPEs were decreased from 41.97 to 18.97 even when one kind of NIR image was added to the RGB image. The highest accuracy for food type classification was 99.81% when using 19 images and the lowest MAPE for caloric content was 10.56 when using 14 images. These results demonstrated that the use of the images captured at various wavelengths in the UV and NIR bands was very helpful for improving the accuracy of food classification and caloric estimation. Full article

(This article belongs to the Special Issue Applications of Spectroscopy Combined Machine Learning in Food Quality and Safety)

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