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2 pages, 186 KiB

Open AccessEditorial

Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain

by Giacomo Squeo and José Manuel Amigo

Foods 2023, 12(16), 3041; https://doi.org/10.3390/foods12163041 - 13 Aug 2023

Cited by 1 | Viewed by 1634

Abstract

Forty years ago, Near InfraRed (NIR) was considered a sleeping technique among the spectroscopic ones [...] Full article

(This article belongs to the Special Issue Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain)

Research

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11 pages, 3859 KiB

Open AccessArticle

Statistic and Network Features of RGB and Hyperspectral Imaging for Determination of Black Root Mold Infection in Apples

by Wen Sha, Kang Hu and Shizhuang Weng

Foods 2023, 12(8), 1608; https://doi.org/10.3390/foods12081608 - 10 Apr 2023

Cited by 11 | Viewed by 2034

Abstract

Apples damaged by black root mold (BRM) lose moisture, vitamins, and minerals as well as carry dangerous toxins. Determination of the infection degree can allow for customized use of apples, reduce financial losses, and ensure food safety. In this study, red-green-blue (RGB) imaging [...] Read more.

Apples damaged by black root mold (BRM) lose moisture, vitamins, and minerals as well as carry dangerous toxins. Determination of the infection degree can allow for customized use of apples, reduce financial losses, and ensure food safety. In this study, red-green-blue (RGB) imaging and hyperspectral imaging (HSI) are combined to detect the infection degree of BRM in apple fruits. First, RGB and HSI images of healthy, mildly, moderately, and severely infected fruits are measured, and those with effective wavelengths (EWs) are screened from HSI by random frog. Second, the statistic and network features of images are extracted by using color moment and convolutional neural network. Meanwhile, random forest (RF), K-nearest neighbor, and support vector machine are used to construct classification models with the above two features of RGB and HSI images of EWs. Optimal results with the 100% accuracy of training set and 96% accuracy of prediction set are obtained by RF with the statistic and network features of the two images, outperforming the other cases. The proposed method furnishes an accurate and effective solution for determining the BRM infection degree in apples. Full article

(This article belongs to the Special Issue Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain)

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20 pages, 5397 KiB

Open AccessFeature PaperArticle

Identifying the “Dangshan” Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image

by Yuanfeng Chen, Li Liu, Yuan Rao, Xiaodan Zhang, Wu Zhang and Xiu Jin

Foods 2023, 12(6), 1178; https://doi.org/10.3390/foods12061178 - 10 Mar 2023

Cited by 6 | Viewed by 2026

Abstract

The “Dangshan” pear woolliness response is a physiological disease that causes large losses for fruit farmers and nutrient inadequacies.The cause of this disease is predominantly a shortage of boron and calcium in the pear and water loss from the pear. This paper used [...] Read more.

The “Dangshan” pear woolliness response is a physiological disease that causes large losses for fruit farmers and nutrient inadequacies.The cause of this disease is predominantly a shortage of boron and calcium in the pear and water loss from the pear. This paper used the fusion of near-infrared Spectroscopy (NIRS) and Computer Vision Technology (CVS) to detect the woolliness response disease of “Dangshan” pears. This paper employs the merging of NIRS features and image features for the detection of “Dangshan” pear woolliness response disease. Near-infrared Spectroscopy (NIRS) reflects information on organic matter containing hydrogen groups and other components in various biochemical structures in the sample under test, and Computer Vision Technology (CVS) captures image information on the disease. This study compares the results of different fusion models. Compared with other strategies, the fusion model combining spectral features and image features had better performance. These fusion models have better model effects than single-feature models, and the effects of these models may vary according to different image depth features selected for fusion modeling. Therefore, the model results of fusion modeling using different image depth features are further compared. The results show that the deeper the depth model in this study, the better the fusion modeling effect of the extracted image features and spectral features. The combination of the MLP classification model and the Xception convolutional neural classification network fused with the NIR spectral features and image features extracted, respectively, was the best combination, with accuracy (0.972), precision (0.974), recall (0.972), and F1 (0.972) of this model being the highest compared to the other models. This article illustrates that the accuracy of the “Dangshan” pear woolliness response disease may be considerably enhanced using the fusion of near-infrared spectra and image-based neural network features. It also provides a theoretical basis for the nondestructive detection of several techniques of spectra and pictures. Full article

(This article belongs to the Special Issue Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain)

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15 pages, 2941 KiB

Open AccessArticle

In-Line Near-Infrared Spectroscopy Gives Rapid and Precise Assessment of Product Quality and Reveals Unknown Sources of Variation—A Case Study from Commercial Cheese Production

by Lars Erik Solberg, Jens Petter Wold, Katinka Dankel, Jorun Øyaas and Ingrid Måge

Foods 2023, 12(5), 1026; https://doi.org/10.3390/foods12051026 - 28 Feb 2023

Cited by 6 | Viewed by 2938

Abstract

Quality testing in the food industry is usually performed by manual sampling and at/off-line laboratory analysis, which is labor intensive, time consuming, and may suffer from sampling bias. For many quality attributes such as fat, water and protein, in-line near-infrared spectroscopy (NIRS) is [...] Read more.

Quality testing in the food industry is usually performed by manual sampling and at/off-line laboratory analysis, which is labor intensive, time consuming, and may suffer from sampling bias. For many quality attributes such as fat, water and protein, in-line near-infrared spectroscopy (NIRS) is a viable alternative to grab sampling. The aim of this paper is to document some of the benefits of in-line measurements at the industrial scale, including higher precision of batch estimates and improved process understanding. Specifically, we show how the decomposition of continuous measurements in the frequency domain, using power spectral density (PSD), may give a useful view of the process and serve as a diagnostic tool. The results are based on a case regarding the large-scale production of Gouda-type cheese, where in-line NIRS was implemented to replace traditional laboratory measurements. In conclusion, the PSD of in-line NIR predictions revealed unknown sources of variation in the process that could not have been discovered using grab sampling. PSD also gave the dairy more reliable data on key quality attributes, and laid the foundation for future improvements. Full article

(This article belongs to the Special Issue Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain)

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14 pages, 10942 KiB

Open AccessArticle

Visualization of Sugar Content Distribution of White Strawberry by Near-Infrared Hyperspectral Imaging

by Hayato Seki, Te Ma, Haruko Murakami, Satoru Tsuchikawa and Tetsuya Inagaki

Foods 2023, 12(5), 931; https://doi.org/10.3390/foods12050931 - 22 Feb 2023

Cited by 18 | Viewed by 3644

Abstract

In this study, an approach to visualize the spatial distribution of sugar content in white strawberry fruit flesh using near-infrared hyperspectral imaging (NIR-HSI; 913–2166 nm) is developed. NIR-HSI data collected from 180 samples of “Tochigi iW1 go” white strawberries are investigated. In order [...] Read more.

In this study, an approach to visualize the spatial distribution of sugar content in white strawberry fruit flesh using near-infrared hyperspectral imaging (NIR-HSI; 913–2166 nm) is developed. NIR-HSI data collected from 180 samples of “Tochigi iW1 go” white strawberries are investigated. In order to recognize the pixels corresponding to the flesh and achene on the surface of the strawberries, principal component analysis (PCA) and image processing are conducted after smoothing and standard normal variate (SNV) pretreatment of the data. Explanatory partial least squares regression (PLSR) analysis is performed to develop an appropriate model to predict Brix reference values. The PLSR model constructed from the raw spectra extracted from the flesh region of interest yields high prediction accuracy with an RMSEP and

R^{2}_{p}

values of 0.576 and 0.841, respectively, and with a relatively low number of PLS factors. The Brix heatmap images and violin plots for each sample exhibit characteristics feature of sugar content distribution in the flesh of the strawberries. These findings offer insights into the feasibility of designing a noncontact system to monitor the quality of white strawberries. Full article

(This article belongs to the Special Issue Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain)

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15 pages, 2287 KiB

Open AccessArticle

Use of Near-Infrared Spectroscopy to Discriminate DFD Beef and Predict Meat Quality Traits in Autochthonous Breeds

by David Tejerina, Mamen Oliván, Susana García-Torres, Daniel Franco and Verónica Sierra

Foods 2022, 11(20), 3274; https://doi.org/10.3390/foods11203274 - 20 Oct 2022

Cited by 6 | Viewed by 3190

Abstract

The potential of near-infrared reflectance spectroscopy (NIRS) to discriminate Normal and DFD (dark, firm, and dry) beef and predict quality traits in 129 Longissimus thoracis (LT) samples from three Spanish purebreeds, Asturiana de los Valles (AV; n = 50), Rubia Gallega (RG; n [...] Read more.

The potential of near-infrared reflectance spectroscopy (NIRS) to discriminate Normal and DFD (dark, firm, and dry) beef and predict quality traits in 129 Longissimus thoracis (LT) samples from three Spanish purebreeds, Asturiana de los Valles (AV; n = 50), Rubia Gallega (RG; n = 37), and Retinta (RE; n = 42) was assessed. The results obtained by partial least squares-discriminant analysis (PLS-DA) indicated successful discrimination between Normal and DFD samples of meat from AV and RG (with sensitivity over 93% for both and specificity of 100 and 72%, respectively), while RE and total sample sets showed poorer results. Soft independent modelling of class analogies (SIMCA) showed 100% sensitivity for DFD meat in total, AV, RG, and RE sample sets and over 90% specificity for AV, RG, and RE, while it was very low for the total sample set (19.8%). NIRS quantitative models by partial least squares regression (PLSR) allowed reliable prediction of color parameters (CIE L*, a*, b*, hue, chroma). Results from qualitative and quantitative assays are interesting in terms of early decision making in the meat production chain to avoid economic losses and food waste. Full article

(This article belongs to the Special Issue Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain)

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10 pages, 404 KiB

Open AccessArticle

Near-Infrared Reflectance Spectrophotometry (NIRS) Application in the Amino Acid Profiling of Quality Protein Maize (QPM)

by Emmanuel Oladeji Alamu, Abebe Menkir, Michael Adesokan, Segun Fawole and Busie Maziya-Dixon

Foods 2022, 11(18), 2779; https://doi.org/10.3390/foods11182779 - 9 Sep 2022

Cited by 12 | Viewed by 2579

Abstract

The accurate quantification of amino acids in maize breeding programs is challenging due to the high cost of analysis using High-Performance Liquid Chromatography (HPLC) and other conventional methods. Using the Near-Infrared Spectroscopic (NIRS) method in breeding to screen many genotypes has proven to [...] Read more.

The accurate quantification of amino acids in maize breeding programs is challenging due to the high cost of analysis using High-Performance Liquid Chromatography (HPLC) and other conventional methods. Using the Near-Infrared Spectroscopic (NIRS) method in breeding to screen many genotypes has proven to be a fast, cost-effective, and non-destructive method. Thus, this study aimed to develop and apply the NIRS prediction models for quantifying amino acids in biofortified quality protein maize (QPM). Sixty-three (63) QPM maize genotypes were used as the calibration set, and another twenty (20) genotypes were used as the validation set. The microwave hydrolysis system coupled with post-column derivatization with 6-amino-quinoline-succinimidyl-carbamate as the derivatization reagent and the HPLC method were used to generate the reference data set used for the calibration development. The calibration models were developed for essential and non-essential amino acids using WINSI Foss software. Good coefficients of determination in calibration (R²_cal) of 0.91, 0.93, 0.93, and 0.91 and low standard errors in calibrations (SEC) of 0.62, 0.71, 0.26, and 1.75 were obtained for glutamic acids, alanine, proline, and leucine, respectively, while aspartic acids, serine, glycine, arginine, tyrosine, valines, and phenylalanine had fairly good R²_Cal values of 0.86, 0.71, 0.81, 0.78, 0.68, 0.79, and 0.75. In contrast, poor (R²_cal) was obtained for histidine (0.07), cystine (0.09), methionine (0.09), lysine (0.20), threonine (0.51), and isoleucine (0.09), respectively. The models’ prediction performances (R²_pred) and standard error of prediction (SEP) were reasonably good for certain amino acids such as aspartic acid (0.90), glycine (0.80), arginine (0.94), alanine (0.90), proline (0.80), tyrosine (0.83), valine (0.82), leucine (0.90), and phenylalanine (0.88) with SEP values of 0.24, 0.39,0.24, 0.93, 0.47,0.34, 0.78, 2.20, and 0.77, respectively. However, certain amino acids had their R²_pred below 0.50, which could be improved to become useful for screening purposes for those amino acids. Further work is recommended by including a training set representing the sample population’s variance to improve the model’s performance. Full article

(This article belongs to the Special Issue Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain)

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13 pages, 3031 KiB

Open AccessArticle

In-Line Estimation of Fat Marbling in Whole Beef Striploins (Longissimus lumborum) by NIR Hyperspectral Imaging. A Closer Look at the Role of Myoglobin

by Jens Petter Wold, Lars Erik Solberg, Mari Øvrum Gaarder, Mats Carlehøg, Karen Wahlstrøm Sanden and Rune Rødbotten

Foods 2022, 11(9), 1219; https://doi.org/10.3390/foods11091219 - 22 Apr 2022

Cited by 10 | Viewed by 2728

Abstract

Fat marbling, the amount, and distribution of intramuscular fat, is an important quality trait for beef loin (Longissimus lumborum) and is closely connected to sensory properties such as tenderness, juiciness, and flavor. For meat producers, it would be of value to [...] Read more.

Fat marbling, the amount, and distribution of intramuscular fat, is an important quality trait for beef loin (Longissimus lumborum) and is closely connected to sensory properties such as tenderness, juiciness, and flavor. For meat producers, it would be of value to grade and sort whole loins according to marbling on the production line. The main goal of this study was to evaluate high-speed NIR hyperspectral imaging in interaction mode (760–1047 nm) for in-line measurement of sensory assessed marbling in both intact loins and loin slices. The NIR system was calibrated based on 28 whole striploins and 412 slices. Marbling scores were assessed for all slices on a scale from 1 to 9 by a trained sensory panel. The calibrated NIR system was tested for in-line measurements on 30 loins and 60 slices at a commercial meat producer. Satisfactory accuracy for prediction of marbling was obtained by partial least squares regression for both slices and whole loins (R² = 0.81 & 0.82, RMSEP = 0.95 & 0.88, respectively). The concentration of myoglobin in the meat and its state of oxygenation has a strong impact on the NIR spectra and can give deviations in the estimated marbling scores. This must be carefully considered in industrial implementation. Full article

(This article belongs to the Special Issue Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain)

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