Foods

Research

15 pages, 2255 KiB

Open AccessArticle

Digital Prediction of the Purchase Price of Fresh Tea Leaves of Enshi Yulu Based on Near-Infrared Spectroscopy Combined with Multivariate Analysis

by Shengpeng Wang, Lin Feng, Panpan Liu, Anhui Gui, Jing Teng, Fei Ye, Xueping Wang, Jinjin Xue, Shiwei Gao and Pengcheng Zheng

Foods 2023, 12(19), 3592; https://doi.org/10.3390/foods12193592 - 27 Sep 2023

Cited by 2 | Viewed by 1292

Abstract

In this study, near-infrared spectroscopy (NIRS) combined with a variety of chemometrics methods was used to establish a fast and non-destructive prediction model for the purchase price of fresh tea leaves. Firstly, a paired t-test was conducted on the quality index (QI) [...] Read more.

In this study, near-infrared spectroscopy (NIRS) combined with a variety of chemometrics methods was used to establish a fast and non-destructive prediction model for the purchase price of fresh tea leaves. Firstly, a paired t-test was conducted on the quality index (QI) of seven quality grade fresh tea samples, all of which showed statistical significance (p < 0.05). Further, there was a good linear relationship between the QI, quality grades, and purchase price of fresh tea samples, with the determination coefficient being greater than 0.99. Then, the original near-infrared spectra of fresh tea samples were obtained and preprocessed, with the combination (standard normal variable (SNV) + second derivative (SD)) as the optimal preprocessing method. Four spectral intervals closely related to fresh tea prices were screened using the synergy interval partial least squares (si-PLS), namely 4377.62 cm⁻¹–4751.74 cm⁻¹, 4755.63 cm⁻¹–5129.75 cm⁻¹, 6262.70 cm⁻¹–6633.93 cm⁻¹, and 7386 cm⁻¹–7756.32 cm⁻¹, respectively. The genetic algorithm (GA) was applied to accurately extract 70 and 33 feature spectral data points from the whole denoised spectral data (DSD) and the four characteristic spectral intervals data (FSD), respectively. Principal component analysis (PCA) was applied, respectively, on the data points selected, and the cumulative contribution rates of the first three PCs were 99.856% and 99.852%. Finally, the back propagation artificial neural (BP-ANN) model with a 3-5-1 structure was calibrated with the first three PCs. When the transfer function was logistic, the best results were obtained (R_p² = 0.985, RMSEP = 6.732 RMB/kg) by 33 feature spectral data points. The detection effect of the best BP-ANN model by 14 external samples were R² = 0.987 and RMSEP = 6.670 RMB/kg. The results of this study have achieved real-time, non-destructive, and accurate evaluation and digital display of purchase prices of fresh tea samples by using NIRS technology. Full article

(This article belongs to the Special Issue Sensors for Food Safety and Quality Assessment)

► Show Figures

Figure 1

24 pages, 7850 KiB

Open AccessArticle

Qualitative and Quantitative Assessments of Apple Quality Using Vis Spectroscopy Combined with Improved Particle-Swarm-Optimized Neural Networks

by Wenping Peng, Zhong Ren, Junli Wu, Chengxin Xiong, Longjuan Liu, Bingheng Sun, Gaoqiang Liang and Mingbin Zhou

Foods 2023, 12(10), 1991; https://doi.org/10.3390/foods12101991 - 15 May 2023

Cited by 2 | Viewed by 3061

Abstract

Exploring a cost-effective and high-accuracy optical detection method is of great significance in promoting fruit quality evaluation and grading sales. Apples are one of the most widely economic fruits, and a qualitative and quantitative assessment of apple quality based on soluble solid content [...] Read more.

Exploring a cost-effective and high-accuracy optical detection method is of great significance in promoting fruit quality evaluation and grading sales. Apples are one of the most widely economic fruits, and a qualitative and quantitative assessment of apple quality based on soluble solid content (SSC) was investigated via visible (Vis) spectroscopy in this study. Six pretreatment methods and principal component analysis (PCA) were utilized to enhance the collected spectra. The qualitative assessment of apple SSC was performed using a back-propagation neural network (BPNN) combined with second-order derivative (SD) and Savitzky–Golay (SG) smoothing. The SD-SG-PCA-BPNN model’s classification accuracy was 87.88%. To improve accuracy and convergence speed, a dynamic learning rate nonlinear decay (DLRND) strategy was coupled with the model. After that, particle swarm optimization (PSO) was employed to optimize the model. The classification accuracy was 100% for testing apples via the SD-SG-PCA-PSO-BPNN model combined with a Gaussian DLRND strategy. Then, quantitative assessments of apple SSC values were performed. The correlation coefficient (r) and root-square-mean error for prediction (RMSEP) in testing apples were 0.998 and 0.112 °Brix, surpassing a commercial fructose meter. The results demonstrate that Vis spectroscopy combined with the proposed synthetic model has significant value in qualitative and quantitative assessments of apple quality. Full article

(This article belongs to the Special Issue Sensors for Food Safety and Quality Assessment)

► Show Figures

Graphical abstract

14 pages, 1468 KiB

Open AccessArticle

An Automated Image Processing Module for Quality Evaluation of Milled Rice

by Chinmay Kurade, Maninder Meenu, Sahil Kalra, Ankur Miglani, Bala Chakravarthy Neelapu, Yong Yu and Hosahalli S. Ramaswamy

Foods 2023, 12(6), 1273; https://doi.org/10.3390/foods12061273 - 16 Mar 2023

Cited by 8 | Viewed by 3664

Abstract

The paper demonstrates a low-cost rice quality assessment system based on image processing and machine learning (ML) algorithms. A Raspberry-Pi based image acquisition module was developed to extract the structural and geometric features from 3081 images of eight different varieties of rice grains. [...] Read more.

The paper demonstrates a low-cost rice quality assessment system based on image processing and machine learning (ML) algorithms. A Raspberry-Pi based image acquisition module was developed to extract the structural and geometric features from 3081 images of eight different varieties of rice grains. Based on features such as perimeter, area, solidity, roundness, compactness, and shape factor, an automatic identification system is developed to segment the grains based on their types and classify them by using seven machine learning algorithms. These ML models are trained using the images and are compared using different ML models. ROC curves are plotted for each model for quantitative analysis to assess the model’s performance. It is concluded that the random forest classifier presents an accuracy of 77 percent and is the best-performing model for the classification of rice varieties. Furthermore, the same algorithm is efficiently employed to determine the price of adulterated rice samples based upon the market price of individual rice. Full article

(This article belongs to the Special Issue Sensors for Food Safety and Quality Assessment)

► Show Figures

Figure 1

16 pages, 23371 KiB

Open AccessArticle

Deep Learning-Based Intelligent Apple Variety Classification System and Model Interpretability Analysis

by Fanqianhui Yu, Tao Lu and Changhu Xue

Foods 2023, 12(4), 885; https://doi.org/10.3390/foods12040885 - 19 Feb 2023

Cited by 12 | Viewed by 5701

Abstract

In this study, series networks (AlexNet and VGG-19) and directed acyclic graph (DAG) networks (ResNet-18, ResNet-50, and ResNet-101) with transfer learning were employed to identify and classify 13 classes of apples from 7439 images. Two training datasets, model evaluation metrics, and three visualization [...] Read more.

In this study, series networks (AlexNet and VGG-19) and directed acyclic graph (DAG) networks (ResNet-18, ResNet-50, and ResNet-101) with transfer learning were employed to identify and classify 13 classes of apples from 7439 images. Two training datasets, model evaluation metrics, and three visualization methods were used to objectively assess, compare, and interpret five Convolutional Neural Network (CNN)-based models. The results show that the dataset configuration had a significant impact on the classification results, as all models achieved over 96.1% accuracy on dataset A (training-to-testing = 2.4:1.0) compared to 89.4–93.9% accuracy on dataset B (training-to-testing = 1.0:3.7). VGG-19 achieved the highest accuracy of 100.0% on dataset A and 93.9% on dataset B. Moreover, for networks of the same framework, the model size, accuracy, and training and testing times increased as the model depth (number of layers) increased. Furthermore, feature visualization, strongest activations, and local interpretable model-agnostic explanations techniques were used to show the understanding of apple images by different trained models, as well as to reveal how and why the models make classification decisions. These results improve the interpretability and credibility of CNN-based models, which provides guidance for future applications of deep learning methods in agriculture. Full article

(This article belongs to the Special Issue Sensors for Food Safety and Quality Assessment)

► Show Figures

Figure 1

13 pages, 1059 KiB

Open AccessArticle

Determination of Possible Adulteration and Quality Assessment in Commercial Honey

by Didem P. Aykas

Foods 2023, 12(3), 523; https://doi.org/10.3390/foods12030523 - 24 Jan 2023

Cited by 8 | Viewed by 2933

Abstract

This study aims to predict several quality traits in commercial honey samples simultaneously and to reveal possible honey adulteration using a field-deployable portable infrared spectrometer without any sample preparation. A total of one hundred and forty-seven commercial honey samples were purchased from local [...] Read more.

This study aims to predict several quality traits in commercial honey samples simultaneously and to reveal possible honey adulteration using a field-deployable portable infrared spectrometer without any sample preparation. A total of one hundred and forty-seven commercial honey samples were purchased from local and online markets in Turkey and the United States of America (USA), and their soluble solids (°Brix), pH, free acidity, moisture, water activity (aw), glucose, fructose, sucrose, and hydroxymethyl furfural (HMF) contents were determined using reference methods. The HMF (n = 11 samples) and sucrose (n = 21) concentrations were higher than the regulatory limits in some tested samples. The exceeding HMF content may imply temperature abuse during storage and prolonged storing. On the other hand, high sucrose content may indicate possible adulteration with commercial sweeteners. Therefore, soft independent modeling of class analogies (SIMCA) analysis was conducted to reveal this potential sweetener adulteration in the samples, and the SIMCA model was able to identify all the flagged samples. The suggested FT-IR technique may be helpful in regulatory bodies in determining honey authenticity issues as well as assessing the quality characteristics of honey samples in a shorter period and at a lower cost. Full article

(This article belongs to the Special Issue Sensors for Food Safety and Quality Assessment)

► Show Figures

Figure 1

11 pages, 2325 KiB

Open AccessArticle

Visual Monitoring of Fatty Acid Degradation during Green Tea Storage by Hyperspectral Imaging

by Yiyi Zhang, Lunfang Huang, Guojian Deng and Yujie Wang

Foods 2023, 12(2), 282; https://doi.org/10.3390/foods12020282 - 7 Jan 2023

Cited by 5 | Viewed by 2299

Abstract

The reduction in freshness during green tea storage leads to a reduction in its commercial value and consumer acceptance, which is thought to be related to the oxidation of fatty acids. Here, we developed a novel and rapid method for the assessment of [...] Read more.

The reduction in freshness during green tea storage leads to a reduction in its commercial value and consumer acceptance, which is thought to be related to the oxidation of fatty acids. Here, we developed a novel and rapid method for the assessment of green tea freshness during storage. Hyperspectral images of green tea during storage were acquired, and fatty acid profiles were detected by GC–MS. Partial least squares (PLS) analysis was used to model the association of spectral data with fatty acid content. In addition, competitive adaptive reweighted sampling (CARS) was employed to select the characteristic wavelengths and thus simplify the model. The results show that the constructed CARS-PLS can achieve accurate prediction of saturated and unsaturated fatty acid content, with residual prediction deviation (RPD) values over 2. Ultimately, chemical imaging was used to visualize the distribution of fatty acids during storage, thus providing a fast and nondestructive method for green tea freshness evaluation. Full article

(This article belongs to the Special Issue Sensors for Food Safety and Quality Assessment)

► Show Figures

Figure 1

13 pages, 3199 KiB

Open AccessArticle

Copper-Modified Double-Emission Carbon Dots for Rapid Detection of Thiophanate Methyl in Food

by Xiaona Yue, Chunna Zhu, Rongrong Gu, Juan Hu, Yang Xu, Sheng Ye and Jing Zhu

Foods 2022, 11(21), 3336; https://doi.org/10.3390/foods11213336 - 24 Oct 2022

Cited by 8 | Viewed by 2679

Abstract

The detection of food safety and quality is very significant throughout the food supply. Stable dual-emission copper-modified fluorescent carbon dots (Cu-CDs) were successfully synthesized by a simple and environment-friendly hydrothermal, which was used for the real-time detection of pesticide residues in agricultural products. [...] Read more.

The detection of food safety and quality is very significant throughout the food supply. Stable dual-emission copper-modified fluorescent carbon dots (Cu-CDs) were successfully synthesized by a simple and environment-friendly hydrothermal, which was used for the real-time detection of pesticide residues in agricultural products. By optimizing the reaction conditions, Cu-CDs showed two emission peaks, with the highest fluorescence intensities at 375 and 450 nm. The structure, chemical composition and optical properties of Cu-CDs were investigated by XRD, TEM and IR. The results showed that thiophanate methyl (TM) could induce fluorescence quenching of Cu-CDs with no other ligands by the electron transfer through π-π stacking. The synchronous response of the dual-emission sensor enhanced the specificity of TM, which showed remarkable anti-interference capability. The fluorescence quenching degree of Cu-CDs had a good linear relationship with the TM concentration; the low detection limit for a pear was 0.75 μM, and for an apple, 0.78 μM. The recoveries in the fruit samples were 79.70–91.15% and 81.20–93.55%, respectively, and the relative standard deviations (RSDs) were less than 4.23% for the pear and less than 3.78% for the apple. Thus, our results indicate the feasibility and reliability of our methods in detecting pesticide residues in agricultural products. Full article

(This article belongs to the Special Issue Sensors for Food Safety and Quality Assessment)

► Show Figures

Figure 1

8 pages, 1154 KiB

Open AccessArticle

Improved Model for Starch Prediction in Potato by the Fusion of Near-Infrared Spectral and Textural Data

by Fuxiang Wang and Chunguang Wang

Foods 2022, 11(19), 3133; https://doi.org/10.3390/foods11193133 - 8 Oct 2022

Cited by 4 | Viewed by 2195

Abstract

In this study, visible-near-infrared (VIS-NIR) hyperspectral imaging was combined with a data fusion strategy for the nondestructive assessment of the starch content in intact potatoes. Spectral and textural data were extracted from hyperspectral images and transformed principal component (PC) images, respectively, and a [...] Read more.

In this study, visible-near-infrared (VIS-NIR) hyperspectral imaging was combined with a data fusion strategy for the nondestructive assessment of the starch content in intact potatoes. Spectral and textural data were extracted from hyperspectral images and transformed principal component (PC) images, respectively, and a partial least squares regression (PLSR) prediction model was then established. The results revealed that low-level data fusion could not improve accuracy in predicting starch content. Therefore, to improve prediction accuracy, key variables were selected from the spectral and textural data through competitive adaptive reweighted sampling (CARS) and correlation analysis, respectively, and mid-level data fusion was performed. With a residual predictive deviation (RPD) value > 2, the established PLSR model achieved satisfactory prediction accuracy. Therefore, this study demonstrated that appropriate data fusion can effectively improve the prediction accuracy for starch content and thus aid the sorting of potato starch content in the production line. Full article

(This article belongs to the Special Issue Sensors for Food Safety and Quality Assessment)

► Show Figures

Figure 1

13 pages, 990 KiB

Open AccessArticle

NIR Spectrometric Approach for Geographical Origin Identification and Taste Related Compounds Content Prediction of Lushan Yunwu Tea

by Xiaoli Yan, Yujie Xie, Jianhua Chen, Tongji Yuan, Tuo Leng, Yi Chen, Jianhua Xie and Qiang Yu

Foods 2022, 11(19), 2976; https://doi.org/10.3390/foods11192976 - 23 Sep 2022

Cited by 14 | Viewed by 2534

Abstract

Lushan Yunwu Tea is one of a unique Chinese tea series, and total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio models (TP/FAA) represent its most important taste-related indicators. In this work, a feasibility study was proposed to simultaneously predict the [...] Read more.

Lushan Yunwu Tea is one of a unique Chinese tea series, and total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio models (TP/FAA) represent its most important taste-related indicators. In this work, a feasibility study was proposed to simultaneously predict the authenticity identification and taste-related indicators of Lushan Yunwu tea, using near-infrared spectroscopy combined with multivariate analysis. Different waveband selections and spectral pre-processing methods were compared during the discriminant analysis (DA) and partial least squares (PLS) model-building process. The DA model achieved optimal performance in distinguishing Lushan Yunwu tea from other non-Lushan Yunwu teas, with a correct classification rate of up to 100%. The synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS) algorithms showed considerable advantages in improving the prediction performance of TP, FAA, and TP/FAA. The siPLS algorithms achieved the best prediction results for TP (R_P = 0.9407, RPD = 3.00), FAA (R_P = 0.9110, RPD = 2.21) and TP/FAA (R_P = 0.9377, RPD = 2.90). These results indicated that NIR spectroscopy was a useful and low-cost tool by which to offer definitive quantitative and qualitative analysis for Lushan Yunwu tea. Full article

(This article belongs to the Special Issue Sensors for Food Safety and Quality Assessment)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Sensors for Food Safety and Quality Assessment

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI