Technology using near infrared spectroscopic and multivariate analysis to determine the soluble solids content of citrus fruit

Prediction of Soluble-Solid Content in Citrus Fruit Using Visible-Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm

Citrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determining the internal quality of fruits. The applicability of the VNIR hyperspectral imaging technique for predicting the SSC in citrus fruits was evaluated in this study. A VNIR hyperspectral imaging system with a wavelength range of 400-1000 nm and 100 W light source was used to acquire hyperspectral images from citrus fruits in two orientations (i.e., stem and calyx ends). The SSC prediction model was developed using partial least-squares regression (PLSR). Spectrum preprocessing, effective wavelength selection through competitive adaptive reweighted sampling (CARS), and outlier detection were used to improve the model performance. The performance of each model was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). In the present study, the PLSR model was developed using only a citrus cultivar. The SSC prediction CARS-PLSR model with outliers removed exhibited R2 and RMSE values of approximatively 0.75 and 0.56 °Brix, respectively. The results of this study are expected to be useful in similar fields such as agricultural and food post-harvest management, as well as in the development of an online system for determining the SSC of citrus fruits.

Insights on the Extraction and Analysis of Phenolic Compounds from Citrus Fruits: Green Perspectives and Current Status

Citrus fruits (CF) are highly consumed worldwide, fresh, processed, or prepared as juices and pies. To illustrate the high economic importance of CF, the global production of these commodities in 2021 was around 98 million tons. CF's composition is considered an excellent source of phenolic compounds (PC) as they have a large amount and variety. Since ancient times, PC has been highlighted to promote several benefits related to oxidative stress disorders, such as chronic diseases and cancer. Recent studies suggest that consuming citrus fruits can prevent some of these diseases. However, due to the complexity of citrus matrices, extracting compounds of interest from these types of samples, and identifying and quantifying them effectively, is not a simple task. In this context, several extractive and analytical proposals have been used. This review discusses current research involving CF, focusing mainly on PC extraction and analysis methods, regarding advantages and disadvantages from the perspective of Green Chemistry.

Improvement strategies of food supply chain through novel food processing technologies during COVID-19 pandemic

Coronavirus disease-19 (COVID-19) is a contagious disease caused by a novel corona virus (SARS-CoV-2). No medical intervention has yet succeeded, though vaccine success is expected soon. However, it may take months or years to reach the vaccine to the whole population of the world. Therefore, the technological preparedness is worth to discuss for the smooth running of food processing activities. We have explained the impact of the COVID-19 pandemic on the food supply chain (FSC) and then discussed the technological interventions to overcome these impacts. The novel and smart technologies during food processing to minimize human-to-human and human-to-food contact were compiled. The potential virus-decontamination technologies were also discussed. Finally, we concluded that these technologies would make food processing activities smarter, which would ultimately help to run the FSC smoothly during COVID-19 pandemic.

Nondestructive monitoring storage quality of apples at different temperatures by near-infrared transmittance spectroscopy

Apple is the most widely planted fruit in the world and is popular in consumers because of its rich nutritional value. In this study, the portable near-infrared (NIR) transmittance spectroscopy coupled with temperature compensation and chemometric algorithms was applied to detect the storage quality of apples. The postharvest quality of apples including soluble solids content (SSC), vitamin C (VC), titratable acid (TA), and firmness was evaluated, and the portable spectrometer was used to obtain near-infrared transmittance spectra of apples in the wavelength range of 590-1,200 nm. Mixed temperature compensation method (MTC) was used to reduce the influence of temperature on the models and to improve the adaptability of the models. Then, variable selection methods, such as uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA), were developed to improve the performance of the models by determining characteristic variables and reducing redundancy. Comparing the full spectral models with the models established on variables selected by different variable selection methods, the CARS combined with partial least squares (PLS) showed the best performance with prediction correlation coefficient (R p) and residual predictive deviation (RPD) values of 0.9236, 2.604 for SSC; 0.8684, 2.002 for TA; 0.8922, 2.087 for VC; and 0.8207, 1.992 for firmness, respectively. Results showed that NIR transmittance spectroscopy was feasible to detect postharvest quality of apples during storage.

Determination of invert syrup adulterated in acacia honey by terahertz spectroscopy with different spectral features

BACKGROUND

Invert syrup is a common adulterant in honey falsification, thus generating risk for consumers. Most of the methods developed are tedious and time-consuming for manufactures and consumers. However, terahertz spectroscopy provides analytical information in a simple, rapid, and environmentally friendly manner. Subsequently, 3 kinds of terahertz spectroscopic characteristics data, the absorption coefficient, the slope of the absorption coefficient spectra, and the area of the absorption coefficient spectra, were employed for determination of acacia honey adulterated with invert syrup.

RESULTS

Single linear regression (SLR) models with different terahertz spectroscopic features were adopted to predict the syrup adulterant proportion in acacia honey. The best SLR model used the area of the absorption coefficient, displaying an adjusted correlation coefficient of 0.985 and a root-mean-square error of 3.201. Meanwhile, multiple linear regression (MLR) models using a successive projections algorithm for variables selection were implemented. The MLR model considered the integral area of the absorption coefficient spectra, as the inputs yielded the best result with less variables selected, higher R c 2 and R p 2 , lower root-mean-square error of calibration and prediction, as well as higher residual predictive deviation.

CONCLUSIONS

The results indicate terahertz spectroscopy combined with the integral area of the absorption coefficient spectra is reliable enough for invert syrup proportion quantification in acacia honey and is also a rapid and nondestructive determination method for other honey adulterants. © 2019 Society of Chemical Industry.

Rapid analysis of soluble solid content in navel orange based on visible-near infrared spectroscopy combined with a swarm intelligence optimization method

Navel orange is a very popular fruit which is rich in nutrition necessary to human health. Nowadays, rapid, nondestructive and pollution-free analysis of internal organic compounds of fruit is an important and promising technology. The purpose of this paper is to present a swarm intelligence optimization method to extract the feature information of visible-near infrared (Vis-NIR) spectra of navel orange for rapid and nondestructive analysis of soluble solid content (SSC) in navel orange. This method was developed on particle swarm optimization (PSO) and named as piecewise particle swarm optimization (PPSO). The experimental results showed that the PPSO algorithm proposed in this paper overcame the disadvantage of PSO's premature convergence. The PLS model based on variables selected by PPSO for nondestructively detecting SSC of navel orange yield promising results, as the standard deviation of prediction (SEP) was 0.427°Brix while the standard error of laboratory (SEL) was 0.22°Brix. It indicated that the application of near infrared spectroscopy (NIRS) technology combined with PPSO for rapid analysis of soluble solid content in navel orange was feasible.

Optimizing genetic algorithm-partial least squares model of soluble solids content in Fukumoto navel orange based on visible-near-infrared transmittance spectroscopy using discrete wavelet transform

BACKGROUND

The thick rind of Fukumoto navel orange is a great barrier to light penetration, which makes it difficult to evaluate the internal quality of Fukumoto navel orange accurately by visible-near-infrared (Vis-NIR) transmittance spectroscopy. The information carried by the transmission spectrum is limited. Thus, the application of genetic algorithm (GA) for variable selection may not reach the expected results, and selected variables may contain redundancy. In this paper, we present the use of discrete wavelet transforms for optimizing a GA-partial least squares (PLS) model based on Vis-NIR transmission spectra of Fukumoto navel orange. Haar, Db, Sym, Coif and Bior wavelets were used to compress the spectral data selected by GA. Then a PLS model was established based on the variables compressed by each wavelet function.

RESULTS

The use of Db4, Sym4, Coif2 and Bior3.5 succeeded in further simplification of the GA-PLS model by reducing the number of variables by 40-44% without decreasing the prediction accuracy. The application of Bior3.5 not only could reduce the number of variables in the GA-PLS model by 40%, but also increase the value of correlation coefficient of prediction by 1% and decrease the value of root mean square error of prediction by 3%.

CONCLUSIONS

The results indicated that the combination of GA and discrete wavelet transforms for variable selection in the internal quality assessment of Fukumoto navel orange by Vis-NIR transmittance spectroscopy was feasible. © 2019 Society of Chemical Industry.

Comparison of algorithms for wavelength variables selection from near-infrared (NIR) spectra for quantitative monitoring of yeast (Saccharomyces cerevisiae) cultivations

Rapid monitoring with near-infrared (NIR) spectroscopy of Saccharomyces cerevisiae cultivations was implemented to monitor yeast concentrations. The measurement of one spectrum by using of FT-NIR spectrometer can obtain 1557 wavelength variables. To distinguish which wavelength variables of the collected FT-NIR spectra carry important and relevant information regarding the yeast concentrations, there are three different variables selection approaches, namely genetic algorithm (GA), competitive adaptive reweighted sampling (CARS), and variable combination population analysis (VCPA), were compared in this study. The selected wavelength variables from each method were evaluated using partial least squares (PLS) models to seek the most significant variable combinations for predicting the yeast concentrations. Experimental results showed that the VCPA-PLS model with the best predictive performance was found when using ten principal components (PCs) based on selected eleven characteristic wavelength variables by VCPA algorithm. And the predictive performance indicators of the model were as follows: the root mean square error of prediction (RMSEP) was 0.0680, the coefficient of determination (R_p²) was 0.9924, and the ratio performance deviation (RPD) was 11.8625 in the validation process. Based on the results, it is promising to develop a specific inexpensive NIR sensor for the yeast cultivation process using several light-emitting diodes.

NIR Spectroscopy Coupled Chemometric Algorithms for Rapid Antioxidants Activity Assessment of Chinese Dates (Zizyphus Jujuba Mill.)

In this work, near-infrared spectroscopy coupled the classical PLS and variable selection algorithms; synergy interval-PLS, backward interval-PLS and genetic algorithm-PLS for rapid measurement of the antioxidant activity of Chinese dates. The chemometric analysis of antioxidant activity assays was performed. The built models were investigated using correlation coefficients of calibration and prediction; root mean square error of prediction, root mean square error of cross-validation and residual predictive deviation (RPD). The correlation coefficient for calibration and prediction sets and RPD values ranged from 0.8503 to 0.9897, 0.8463 to 0.9783 and 1.86 to 4.88, respectively. In addition, variable selection algorithms based on efficient information extracted from acquired spectra were superior to classical PLS. The overall results revealed that near-infrared spectroscopy combined with chemometric algorithms could be used for rapid quantification of antioxidant content in Chinese dates samples.

Smart storage technologies applied to fresh foods: A review

Fresh foods are perishable, seasonal and regional in nature and their storage, transportation, and preservation of freshness are quite challenging. Smart storage technologies can online detection and monitor the changes of quality parameters and storage environment of fresh foods during storage, so that operators can make timely adjustments to reduce the loss. This article reviews the smart storage technologies from two aspects: online detection technologies and smartly monitoring technologies for fresh foods. Online detection technologies include electronic nose, nuclear magnetic resonance (NMR), near infrared spectroscopy (NIRS), hyperspectral imaging and computer vision. Smartly monitoring technologies mainly include some intelligent indicators for monitoring the change of storage environment. Smart storage technologies applied to fresh foods need to be highly efficient and nondestructive and need to be competitively priced. In this work, we have critically reviewed the principles, applications, and development trends of smart storage technologies.

On-line monitoring of extraction process of Flos Lonicerae Japonicae using near infrared spectroscopy combined with synergy interval PLS and genetic algorithm

There is a growing need for the effective on-line process monitoring during the manufacture of traditional Chinese medicine to ensure quality consistency. In this study, the potential of near infrared (NIR) spectroscopy technique to monitor the extraction process of Flos Lonicerae Japonicae was investigated. A new algorithm of synergy interval PLS with genetic algorithm (Si-GA-PLS) was proposed for modeling. Four different PLS models, namely Full-PLS, Si-PLS, GA-PLS, and Si-GA-PLS, were established, and their performances in predicting two quality parameters (viz. total acid and soluble solid contents) were compared. In conclusion, Si-GA-PLS model got the best results due to the combination of superiority of Si-PLS and GA. For Si-GA-PLS, the determination coefficient (R_p²) and root-mean-square error for the prediction set (RMSEP) were 0.9561 and 147.6544μg/ml for total acid, 0.9062 and 0.1078% for soluble solid contents, correspondingly. The overall results demonstrated that the NIR spectroscopy technique combined with Si-GA-PLS calibration is a reliable and non-destructive alternative method for on-line monitoring of the extraction process of TCM on the production scale.

Rapid measurement of epimedin A, epimedin B, epimedin C, icariin, and moisture in Herba Epimedii using near infrared spectroscopy

In this work, near infrared (NIR) spectroscopy was used in combination with chemometrics to determine the epimedin A, epimedin B, epimedin C, icariin, and moisture contents of Herba Epimedii. The variable selection method genetic algorithm (GA) and regression tool support vector machine (SVM) were used to improve the model performance. Four different calibration models, namely Full-PLS, GA-PLS, Full-SVM, and GA-SVM, were established, and their performances in terms of prediction accuracy and model robustness were systemically studied and compared. In conclusion, the performances of the models based on the efficient variables selected through GA were better than those based on full spectra, and the nonlinear models were superior over the linear models. In addition, the GA-SVM model demonstrated the optimal performance in predicting five quality parameters (viz. epimedin A, epimedin B, epimedin C, icariin, and moisture). For GA-SVM, the determination coefficient (R_p²), root-mean-square error (RMSEP), and residual predictive deviation (RPD) for the prediction set were 0.9015, 0.0268%, and 2.20 for epimedin A; 0.9089, 0.0656%, and 3.08 for epimedin B; 0.9056, 0.1787%, and 3.18 for epimedin C; 0.8192, 0.0657%, and 2.26 for icariin; and 0.9367, 0.2062%, and 4.12 for moisture, correspondingly. Results indicated that NIR spectroscopy coupled with GA-SVM calibration can be used as a reliable alternative strategy to measure the epimedin A, epimedin B, epimedin C, icariin, and moisture contents of Herba Epimedii because this technique is fast, economic, and nondestructive compared with traditional chemical methods.

Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics

Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of spectra of the jujube (Zizyphus jujuba Mill.) fruit samples from four geographical regions. Prediction models were developed for the quantitative prediction of the contents of jujube fruit, i.e., total sugar, total acid, total phenolic content, and total antioxidant activity. Four pattern recognition methods, principal component analysis (PCA), linear discriminant analysis (LDA), least squares-support vector machines (LS-SVM), and back propagation-artificial neural networks (BP-ANN), were used for the geographical origin classification. Furthermore, three multivariate calibration models based on the standard normal variate (SNV) pretreated NIR spectroscopy, partial least squares (PLS), BP-ANN, and LS-SVM were constructed for quantitative analysis of the four analytes described above. PCA provided a useful qualitative plot of the four types of NIR spectra from the fruit. The LS-SVM model produced best quantitative prediction results. Thus, NIR spectroscopy in conjunction with chemometrics, is a very useful and rapid technique for the discrimination of jujube fruit.