Mean Centering Research Articles

Near-infrared spectroscopy combined with support vector machine for the identification of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) adulteration using wavelength selection algorithms

The frequent occurrence of adulterating Tartary buckwheat powder with crop flours in the market necessitates an urgent need for a simple analysis method to ensure the quality of Tartary buckwheat. This study employed near-infrared spectroscopy (NIRS) for the collection of spectral data from Tartary buckwheat samples adulterated with whole wheat, oat, soybean, barley, and sorghum flours. The competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were deployed to identify informative wavelengths. By integrating support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA), we constructed qualitative models to discern Tartary buckwheat adulteration. The PLS-DA model exhibited prediction accuracies between 89.78 % and 94.22 %, while the mean-centering (MC)-PLS-DA model showcased impressive predictive accuracy of 93.33 %. Notably, the feature-based Autoscales-CARS-CV-SVM model achieved more excellent identification accuracy. These findings exhibit the excellent potential of chemometrics as a powerful tool for detecting food product adulteration.

Fast and simultaneous prediction of inner quality parameters on intact mangos by near infrared spectroscopy: Impact of spectra pre-processing on prediction accuracy

Near infrared spectroscopy or known as NIRS has been widely employed in many fields including agriculture, especially for sorting and grading of agricultural products. Spectra pre-processing is one of the main factors affecting model accuracy and prediction capabilities of NIRS. The objective of the present study was to study the impact of different spectra corrections namely mean centering (MC), mean normalization (MN), de-trending (DT), multiplicative scatter correction (MSC), standard normal variate (SNV) and orthogonal signal correction (OSC), to the prediction accuracy of quality parameters: titratable acidity (TA) and soluble solids content (SSC) in intact mango. A total of 91 mango samples (cv. Kent) were used as dataset for calibration and external prediction which was separated by means of systematic sampling based on a property (SSBP) approach. Diffuse reflectance spectra (log1/R) were acquired and recorded in wavelength range of 1000 – 2500 nm by Antaris Fourier transform NIR instrument. Judging from calibration and prediction performance, MSC found to be the best spectra pre-processing method prior to prediction model development with R2 prediction are 0.72 for TA and 0.76 for SSC. Although MSC increase the prediction performances based on R2, RMSE, RPD and RER metrics compared to the baseline, the achieved RPD, 1.9 for TA and 1.8 for SSC of this findings are still poor and need improvements to achieve even higher levels of accuracy and reliability necessitates for real-time applications.

Optimization of a tensile strength prediction model for compacted ribbons using NIR-HIS analysis

The tensile strength (TS) of compacted ribbon is a critical quality attribute in the roller compaction process that impacts the quality of the finished product. This study investigated the use of Near Infrared Hyperspectral Imaging Spectroscopy (NIR-HIS) technology for predicting TS of compacted ribbons, considering the effects of surface curvature, different spectral preprocessing methods, and variable selection methods on a predictive model based on Partial Least Squares regression (PLSr). The spectral preprocessing methods evaluated were Mean Centering (MC) and Standard Normal Variate (SNV). The variable selection methods were Filter by Regression Coefficient (REG), Variable Importance in Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Genetic Algorithm (GA). The results indicated that curved surfaces had no significant impact on the predictive performance of the model (p-value of 0.39 for RMSEP). The PLSr-CARS method, combined with MC spectral preprocessing, was successful in selecting and reducing the number of wavelengths from 182 to 5, as indicated by high values of R2pred and RPD, and a low RMSEP value (0.97, 5.75, and 7.60%, respectively). An MLR model using the 5 wavelengths was also developed, showing similar performance to the PLSr model. Both the MLR and PLSr models demonstrated high predictive accuracy and reliability. These models can perform well even when developed using only a few wavelengths, leading to significant reductions in processing time and measurement costs, making them valuable tools for quality control in the pharmaceutical industry.

Novel authentication of African geographical coffee types (bean, roasted, powdered) by handheld NIR spectroscopic method

African coffee is among the best traded coffee types worldwide, and rapid identification of its geographical origin is very important when trading the commodity. The study was important because it used NIR techniques to geographically differentiate between various types of coffee and provide a supply chain traceability method to avoid fraud. In this study, geographic differentiation of African coffee types (bean, roasted, and powder) was achieved using handheld near-infrared spectroscopy and multivariant data processing. Five African countries were used as the origins for the collection of Robusta coffee. The samples were individually scanned at a wavelength of 740–1070 nm, and their spectra profiles were preprocessed with mean centering (MC), multiplicative scatter correction (MSC), and standard normal variate (SNV). Support vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN), random forests (RF), and partial least square discriminate analysis (PLS-DA) were then used to develop a prediction model for African coffee types. The performance of the model was assessed using accuracy and F1-score. Proximate chemical composition was also conducted on the raw and roasted coffee types. The best classification algorithms were developed for the following coffee types: raw bean coffee, SD-PLSDA, and MC + SD-PLSDA. These models had an accuracy of 0.87 and an F1-score of 0.88. SNV + SD-SVM and MSC + SD-NN both had accuracy and F1 scores of 0.97 for roasted coffee beans and 0.96 for roasted coffee powder, respectively. The results revealed that efficient quality assurance may be achieved by using handheld NIR spectroscopy combined with chemometrics to differentiate between different African coffee types according to their geographical origins.

Fraud detection in the fishing sector using hyperspectral imaging

Currently, more and more consumers are interested in the quality, safety, and authenticity of food products. The fishing sector is the second food category with the highest risk of fraud and the greatest presence of authentication problems. There are non-destructive, fast and accurate techniques for real-time authentication, with hyperspectral imaging (HSI) standing out among these. In this context, the main aim of this study is to explore the viability of HSI in the visible and near infrared (VIS-NIR) and near infrared (NIR) ranges for the detection of fraud by origin and by non-declaration of the previous freezing process, in anchovies. The spectral pretreatment methods used were the standard normal variate method, the Savitzky-Golay 1st derivate and the Savitzky-Golay 2nd derivate, always followed by mean centering (MC). In addition, the impact of using a previous step of smoothing prior to pretreatment was also evaluated. Two classification algorithms: soft independent modeling of class analogy, and partial least squares discriminant analysis (PLS-DA) were used to build the classification model. After analysis, it was found that the modelling results using the VIS-NIR region were always better than those using the NIR region, and the best performing model was by PLS-DA with a recall of 0.97 for fresh and 0.98 for frozen-thawed anchovies and 0.98 for Cantabrian anchovies and 0.96 for Mediterranean anchovies. One advantage of the model obtained is the ability to classify the anchovies measuring on the skin side of fish without the need for sample preparation. Overall, the results showed that HSI combined with PLS-DA is a favorable solution for rapid, and non-destructive recognition of adulteration regarding freshness and origin in anchovies.

Non-destructive prediction of anthocyanin concentration in whole eggplant peel using hyperspectral imaging

Accurately detecting the anthocyanin content in eggplant peel is essential for effective eggplant breeding. The present study aims to present a method that combines hyperspectral imaging with advanced computational analysis to rapidly, non-destructively, and precisely measure anthocyanin content in eggplant fruit. For this purpose, hyperspectral images of the fruits of 20 varieties with diverse colors were collected, and the content of the anthocyanin were detected using high performance liquid chromatography (HPLC) methods. In order to minimize background noise in the hyperspectral images, five preprocessing algorithms were utilized on average reflectance spectra: standard normalized variate (SNV), autoscales (AUT), normalization (NOR), Savitzky–Golay convolutional smoothing (SG), and mean centering (MC). Additionally, the competitive adaptive reweighted sampling (CARS) method was employed to reduce the dimensionality of the high-dimensional hyperspectral data. In order to predict the cyanidin, petunidin, delphinidin, and total anthocyanin content of eggplant fruit, two models were constructed: partial least squares regression (PLSR) and least squares support vector machine (LS-SVM). The HPLC results showed that eggplant peel primarily contains three types of anthocyanins. Furthermore, there were significant differences in the average reflectance rates between 400–750 nm wavelength ranges for different colors of eggplant peel. The prediction model results indicated that the model based on NOR CARS LS-SVM achieved the best performance, with a squared coefficient of determination (R2) greater than 0.98, RMSEP and RMSEC less than 0.03 for cyanidin, petunidin, delphinidin, and total anthocyanin predication. These results suggest that hyperspectral imaging is a rapid and non-destructive technique for assessing the anthocyanin content of eggplant peel. This approach holds promise for facilitating the more effective eggplant breeding.

Geographical origin discrimination of black beans using near-infrared (NIR) spectroscopy combined with fuzzy regularized complete linear discriminant analysis (FRCLDA)

Black beans from different geographical origins exhibit variations in terms of their nutritional and economic values. Therefore, ensuring traceability of the geographical origin of black beans is essential for both consumers and the product processing industries. The study designed a comprehensive black bean origin identification model using near-infrared (NIR) spectroscopy combined with discriminant analysis algorithms. To enhance the performance of the model, this study incorporated fuzzy logic into regularized complete linear discriminant analysis (RCLDA) and proposed the fuzzy regularized complete linear discriminant analysis (FRCLDA). This innovative approach aims to extract additional information from overlapping NIR spectra of black beans. NIR spectra of black beans from five locations were collected employing a portable near-infrared spectrometer. In the preprocessing stage, the Savitzky–Golay (SG) and mean centering (MC) were applied to process the raw spectra. Subsequently, complete linear discriminant analysis (CLDA), RCLDA, and FRCLDA were utilized to extract the information from the processed data. Finally, k-nearest neighbor (KNN), support vector machines (SVM), and extreme learning machine (ELM) were employed to classify the black bean samples based on the extracted features. The results indicated that KNN achieved the best classification performance. The FRCLDA-KNN model achieved the exceptional performance with a classification accuracy of 99.23% on the test set. Therefore, the system demonstrates satisfactory accuracy and performance in accurately identifying black beans from different geographical origins.

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage.

The hardness of passion fruit is a critical feature to consider when determining maturity during post-harvest storage. The capacity of near-infrared diffuse reflectance spectroscopy (NIRS) for non-destructive detection of outer and inner hardness of passion fruit epicarp was investigated in this work. The passion fruits' spectra were obtained using a near-infrared spectrometer with a wavelength range of 10,000-4000 cm-1. The hardness of passion fruit's outer epicarp (F1) and inner epicarp (F2) was then measured using a texture analyzer. Moving average (MA) and mean-centering (MC) techniques were used to preprocess the collected spectral data. Competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) were used to pick feature wavelengths. Grid-search-optimized random forest (Grids-RF) models and genetic-algorithm-optimized support vector regression (GA-SVR) models were created as part of the modeling process. After MC preprocessing and CARS selection, MC-CARS-Grids-RF model with 7 feature wavelengths had the greatest prediction ability for F1. The mean square error of prediction set (RMSEP) was 0.166 gN. Similarly, following MA preprocessing, the MA-Grids-RF model displayed the greatest predictive performance for F2, with an RMSEP of 0.101 gN. When compared to models produced using the original spectra, the R2P for models formed after preprocessing and wavelength selection improved. The findings showed that near-infrared spectroscopy may predict the hardness of passion fruit epicarp, which can be used to identify quality during post-harvest storage.

Smart green spectrophotometric assay of the ternary mixture of drotaverine, caffeine and paracetamol in their pharmaceutical dosage form

Three green and facile spectrophotometric methods were developed for the assay of Petro® components; drotaverine HCl (DRT), caffeine (CAFF), and paracetamol (PAR). The three methods depend on measuring the absorbance of the studied drugs through their ethanolic solution. The first derivative spectrophotometry (FDS) at (Δλ = 10) were good parameters for DRT and CAFF resolution; DRT and CAFF could be well calibrated using FDS at 320 and 285 nm, respectively. PAR could be estimated at 308 nm utilizing the second derivative spectrophotometry (SDS). Method II relies on the double divisor ratio derivative spectroscopy (DDRDS). The first derivative was applied on each drug where they would be assayed at 309, 288, and 255 nm for DRT, CAFF, and PAR, respectively. Method III depends on the mean centering (MCR) technique. DRT, CAFF, and PAR could be determined at 309, 214, and 248 nm, respectively. The concentrations were rectilinear in the ranges of 2–20 µg/mL for DRT, 1.5–15 µg/mL for CAFF, and 2–40 µg/mL for PAR in double devisor and mean centering but PAR from 5 to 40 µg/mL in derivative method. Method validation was performed according to ICH guidelines assured by the agreement with the comparison method. In addition, greenness assessment of the proposed methods was investigated. The application of the proposed method was extended to analyse tablet dosage form and performing invitro dissolution testing.

Auxiliary Diagnosis of Papillary Thyroid Carcinoma Based on Spectral Phenotype.

Thyroid cancer, a common endocrine malignancy, is one of the leading death causes among endocrine tumors. The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures. Therefore, we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma (PTC) diagnosis and characterize PTC characteristics. To alleviate any concerns pathologists may have about using the model, we conducted an analysis of the used bands that can be interpreted pathologically. A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients. The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue. Clinical data of the corresponding patients were collected for subsequent model interpretability analysis. The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University. The spectral preprocessing method was used to process the spectra, and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models. The PTC detection model using mean centering (MC) and multiple scattering correction (MSC) has optimal performance, and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide. For model interpretable analysis, the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak, and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients. Moreover, the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients. In addition, the correlation analysis was performed between the selected wavelengths and the clinical data, and the results show: the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure, nucleus size and free thyroxine (FT4), and has a strong correlation with triiodothyronine (T3); the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine (FT3).

DETERMINATION OF UREA-FORMALDEHYDE RESIN CONTENT IN POPLAR FIBER BASED ON HYPERSPECTRAL TECHNIQUES

In this experiment, poplar fibers containing 0%, 2%, 4%, 6%, 8%, 10%, 12%, 14%, 16%, 18%, 20%, 25% and 30% of urea-formaldehyde resin were prepared. A model for the detection of urea-formaldehyde resin content in poplar fibers was established by the hyperspectral near-infrared imaging system combined with relevant algorithms. The spectral images of poplar fibers containing different contents of urea-formaldehyde (UF) resin were measured separately using hyperspectral imager. The results of four preprocessing methods, namely mean centering (MC), multiple scattering correction (MSC), standard normal variables (SNV) and first-order derivative (1-Der) were analyzed, and the optimal preprocessing method was selected as SNV. The band combinations with the highest correlation with the urea-formaldehyde resin content were compared and analyzed with the full-band model to establish the partial least squares regression (PLSR) model. The experimental results show that the hyperspectral imaging system combined with the corresponding algorithm can achieve rapid detection of UF resin content in poplar fibers, and the results of this study provide technical support and theoretical reference for determination of resin content in ultra-thin fiberboard production. The method is an innovative model for the determination of UF resin in wood fibers.

Detection of Water Content in Lettuce Canopies Based on Hyperspectral Imaging Technology under Outdoor Conditions

To solve the problem of non-destructive crop water content of detection under outdoor conditions, we propose a method to predict lettuce canopy water content by collecting outdoor hyperspectral images of potted lettuce plants and combining spectral analysis techniques and model training methods. Firstly, background noise was removed by correlation segmentation, proposed in this paper, whereby light intensity correction is performed on the segmented lettuce canopy images. We then chose the first derivative combined with mean centering (MC) to preprocess the raw spectral data. Hereafter, feature bands were screened by a combination of Monte Carlo uninformative variable elimination (MCUVE) and competitive adaptive reweighting sampling (CARS) to eliminate redundant information. Finally, a lettuce canopy moisture prediction model was constructed by combining partial least squares (PLS). The correlation coefficient between model predicted and measured values was used as the main model performance evaluation index, and the modeling set correlation coefficient Rc was 82.71%, while the prediction set correlation coefficient RP was 84.67%. The water content of each lettuce canopy pixel was calculated by the constructed model, and the visualized lettuce water distribution map was generated by pseudo-color image processing, which finally revealed a visualization of the water content of the lettuce canopy leaves under outdoor conditions. This study extends the hyperspectral image prediction possibilities of lettuce canopy water content under outdoor conditions.

Smart stability indicating spectrophotometric methods for determination of modafinil: the promising treatment for post-covid neurological syndrome

Modafinil (MDF) is one of the neurostimulants with a potential effect in the COVID-19 ICU ventilated patients and post-COVID neurological syndrome treatment. Four rapid, simple and cost-effective stability indicating spectrophotometric methods were used for estimation of MDF in the presence of its acidic degradation product, namely; ratio difference (RD), first derivative of the ratio spectra (1DD), mean centering (MCR) and ratio subtraction method (RS). These methods were validated according to ICH guidelines and all methods revealed a good linearity in concentration range of (5-30 µg/mL) in addition to a good accuracy and precision with mean percentage recovery of 99.97 ± 0.305 for (RD), 100.10 ± 0.560 for (1DD), 100.02 ± 0.483 for (MCR) & 99.18 ± 1.145 for (RS) method. Specificity of the proposed methods was assessed and MDF was determined in the presence of up to 80% of its acidic degradation product for RD, 1DD, MCR and RS methods. The proposed methods were successfully applied for the determination of MDF in bulk powder and its tablet dosage form with mean percentage recovery of 100.33 ± 0.915 for (RD), 100.62 ± 0.985 for (1DD), 99.70 ± 0.379 for (MCR) and 100.21 ± 0.313 for (RS) method. The results obtained were statistically compared with those of official HPLC method and showed no significant difference with relevance accuracy and precision.

Quantum mean centering for block-encoding-based quantum algorithm

Mean Centering (MC) is an important data preprocessing technique, which has a wide range of applications in data mining, machine learning, and multivariate statistical analysis. When the data set is large, this process will be time-consuming. In this paper, we propose an efficient quantum MC algorithm based on the block-encoding technique, which enables the existing quantum algorithms can get rid of the assumption that the original data set has been classically mean-centered. Specifically, we first adopt the strategy that MC can be achieved by multiplying by the centering matrix C, i.e., removing the row means, column means and row-column means of the original data matrix X can be expressed as XC, CX and CXC, respectively. This allows many classical problems involving MC, such as Principal Component Analysis (PCA), to directly solve the matrix algebra problems related to XC, CX or CXC. Next, we can employ the block-encoding technique to realize MC. To achieve it, we first show how to construct the block-encoding of the centering matrix C, and then further obtain the block-encodings of XC, CX and CXC. Finally, we describe one by one how to apply our MC algorithm to PCA and other algorithms.

Study on Characteristic Raman Shift Screening Method Based on MPA for Raman Spectrum of Mine Water Inrush Source

In the process of mine water inrush disaster prevention, accurate and rapid identification of water inrush source type is of great significance to coal mine safety production. However, traditional hydrochemical methods have shortcomings such as time-consuming and complex detection. Therefore, a new idea of identifying mine water inrush source by Raman spectroscopy is proposed. Goaf water, roof sandstone fissure water, Ordovician limestone water, Taiyuan limestone water, and surface water as well as their mixed water samples are selected as research objects, and Raman spectral data of different water samples are collected by the Raman spectroscopy system. To eliminate the influence of laser power fluctuation and spectrometer system noise in Raman spectrum acquisition, detrend correction (DC), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), first derivative (FD), and mean centering (MC) were used to preprocess the raw Raman spectra. Due to the large dimension and long analysis time of Raman spectrum data, the marine predator algorithm (MPA) is used to screen the characteristic Raman shifts of the Raman spectrum of water samples, and the characteristic Raman shift information that can best characterize the mine water samples is obtained. Finally, to verify the feasibility of MPA screening the characteristic Raman shifts of Raman spectrum of mine water inrush source, the selected characteristic Raman displacement information is used as input to construct BP neural network (BP), k-nearest neighbor algorithm (KNN), support vector machine (SVM), and decision tree (DT) classification models, respectively. Experiments show that SNV has the best preprocessing effect on the raw Raman spectrum, which can effectively eliminate part of the noise in the Raman spectrum data and improve the accuracy of Raman spectrum identification. Using MPA, 226 characteristic Raman shifts can be screened from 2048 Raman data points, reducing the number of Raman shifts to 11.04%, and the modeling accuracy of characteristic Raman shift information screened by MPA is higher than that of full Raman data. In addition, the average analysis speed of BP, KNN, SVM, and DT water source identification models is 7.61 times faster than that of all Raman data. The results show that MPA is adopted to screen the characteristic Raman displacement of mine water source Raman spectrum, which can effectively reduce the redundancy of Raman spectral data and greatly improve the speed of Raman spectral analysis, which is of great significance to ensure the real-time detection of the mine water source.

Advanced Spectrophotometric Resolution Techniques for a Spectrally Overlapping Spasmolytic Binary Mixture along with Dichloroaniline as a Toxic Impurity: Application to Content Uniformity Testing.

Irritable bowel syndrome (IBS) is a common disorder leading to undesirable pain. Phloroglucinol (PHG) and trimethylphloroglucinol (TMG) are co-formulated as spasmolytic medication that is considered to be effective in reducing smooth muscle spasm. 3,5-Dichloroaniline (DCL) is a specified PHG pharmacopoeial impurity which needs to be monitored to avoid its toxic effects. Different smart approaches are presented to provide simple, reliable, and economic spectrophotometric methods able to resolve the severe overlap in the spectra of PHG and TMG in their pure and pharmaceutical forms, in addition to their estimation in the presence of DCL as a toxic impurity of PHG without any need for initial separation. The presented work includes univariate methods, derivative ratio (DR), ratio difference (RD), mean centering (MCR) and deconvulated Fourier method (DF), which were able to determine PHG and TMG simultaneously in their binary mixture. Firstly, DCL was estimated in the zero order, where the two drugs have zero absorption at 247.0 nm, and then its contribution was eliminated by applying ratio subtraction method. Multivariate chemometric partial least squares (PLS) and principal component regression (PCR) models were also applied to determine PHG and TMG simultaneously in the presence of the impurity, DCL. Univariate methods were applied in the range 5.0-30.0, 2.5-25.0, and 1.0-12.0 µg/mL for PHG, TMG, and DCL, respectively. The proposed chemometric models were used in the range 6.0-14.0, 5.0-25.0 and 2.0-10.0 µg/mL for PHG, TMG, and DCL, respectively. These analytical approaches succeeded in estimating the cited drugs in their pharmaceutical formulation and assessing content uniformity of dosage units. The methods were statistically compared with a reported HPLC method, and the results revealed no significance statistical difference. This work provides for the first time successful univariate and multivariate PLS and PCR methods to assess PHG and TMG in the presence of DCL as a toxic impurity along with content uniformity testing of dosage units. Comparative univariate and multivariate spectrophotometric analytical approaches are presented, for the first time, for estimation of spasmolytic formulation of PHG and TMG in the presence of DCL as a PHG toxic impurity. Successful application to content uniformity testing of Stopspasm® dosage form is demonstrated. A statistical study, including t-tests and one-way analysis of variance (ANOVA), was conducted.

Discrimination of the Red Jujube Varieties Using a Portable NIR Spectrometer and Fuzzy Improved Linear Discriminant Analysis.

In order to quickly, nondestructively, and effectively distinguish red jujube varieties, based on the combination of fuzzy theory and improved LDA (iLDA), fuzzy improved linear discriminant analysis (FiLDA) algorithm was proposed to classify near-infrared reflectance (NIR) spectra of red jujube samples. FiLDA shows performs better than iLDA in dealing with NIR spectra containing noise. Firstly, the portable NIR spectrometer was employed to gather the NIR spectra of five kinds of red jujube, and the initial NIR spectra were pretreated by standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (S-G smoothing), mean centering (MC) and Savitzky-Golay filter (S-G filter). Secondly, the high-dimensional spectra were processed for dimension reduction by principal component analysis (PCA). Then, linear discriminant analysis (LDA), iLDA and FiLDA were applied to extract features from the NIR spectra, respectively. Finally, K nearest neighbor (KNN) served as a classifier for the classification of red jujube samples. The highest classification accuracy of this identification system for red jujube, by using FiLDA and KNN, was 94.4%. These results indicated that FiLDA combined with NIR spectroscopy was an available method for identifying the red jujube varieties and this method has wide application prospects.

Classification and authentication of tea according to their geographical origin based on FT-IR fingerprinting using pattern recognition methods

The potential of FT-IR spectra was examined to classify tea samples based on the geographical origins. Principal component analysis (PCA), principal component analysis-linear discriminant analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were investigated in order to achieve discrimination of tea samples. Several spectral pre-processing methods, such as mean centering (MC), auto-scaling, multiplicative scatter correction (MSC), standard normal variate (SNV) and their combinations, were employed to improve the quality of the spectra. The results showed that the tea samples from five geographical regions can be identified based on using FT-IR spectral fingerprints. The results demonstrated that FT-IR spectral fingerprinting combined with pattern recognition methods can be employed as an effective and feasible method for classification of Iranian tea based on their geographical origins.

Research on the online rapid sensing method of moisture content in famous green tea spreading

AbstractMoisture content is an important indicator in the process of green tea spreading. In order to realize the rapid detection of moisture content in the green tea spreading process, this paper collects 200 near‐infrared spectroscopy data during the spreading process and establishes a quantitative model of moisture content. To improve the performance of the model, standard normal variables (SNV), mean centering (MC), max–min normalization (Max–Min), detrending (Detrend), and Savitzky–Golay filtering (SG FIR) are used to improve the signal‐to‐noise ratio of the original spectrum. By comparing the partial least squares regression (PLSR) models established by the different pretreatment methods, SG FIR is determined as the best preprocessing method. To simplify the prediction model, the successive projections algorithm, competitive adaptive reweighted sampling, interval random frog, iterative retained information variables, variable combination population analysis (VCPA), and VCPA‐GA are used to extract the characteristic wavelengths from the preprocessed spectrum by SG FIR method. Then, based on the full spectrum and characteristic variables, the PLSR and support vector regression (SVR) models are established. The results show that established SVR prediction model based on VCPA‐GA extracting the characteristic wavelengths has the best effect. Among them, the correlation coefficients of the calibration set (Rc) is 0.9785, and the correlation coefficients of the prediction set (Rp) is 0.9658. Relative percent deviations are 3.6787. The results show that the VCPA‐GA + SVR model, based on near‐infrared spectroscopy technology, is the most effective and can accurately predict the moisture content in the green tea spreading process.Practical ApplicationsAt present, near‐infrared spectroscopy technology has been widely used in food detection. This study solved the problem of the inability to quantitatively predict the moisture content during the green tea spreading process by discussing different spectral preprocessing methods, characteristic wavelength selection methods and machine learning algorithms, and realized the rapid online detection of moisture during the green tea spreading process. This research has important guiding significance for the intelligentization of green tea processing equipment.

Application of portable near infrared spectroscopy for classifying and quantifying cocoa bean quality parameters

Fermentation duration (FmD), fermentation index (FI), pH, and moisture content (Mc) are vital quality attributes of cocoa beans. In this study, portable near infrared spectroscopy (NIRS) and multivariate analyses were used for rapid determination of FmD, FI, pH, and Mc of cocoa beans. The samples were scanned in 900- to 1,700-nm wavelength, and the spectral data were pretreated independently with first derivatives (FD) and second derivatives (SD), multiplicative scatter correction (MSC), mean centering (MC), and standard normal variate (SNV), while linear discriminant analysis (LDA), support vector machine (SVM), and partial least squares regression (PLS-R) were used to build the prediction models for FmD, FI, pH, and Mc. MSC plus SVM gave an accurate classification of 100%. For predicting FI, pH, and Mc, the PLS-R model gave coefficient of correlation of 0.87, 0.82, and 0.89, respectively. The results showed that portable NIRS could be employed for cocoa bean examination. Novelty impact statement Fermentation is the single most essential postharvest operation that influences cocoa beans quality parameters including moisture content, fermentation index (FI) and pH. Unlike stationary laboratory based wet chemistry technique or table top NIR spectroscopy, this study revealed that the relatively inexpensive portable NIR spectroscopy could provide very fast (within 30 s) results in the routine onsite evaluation of cocoa beans moisture content, fermentation index and pH on farmers field in Sub-Saharan Africa. In particular, the study outcome highlights the potential application of portable NIR spectroscopy based on machine learning for efficient classification of fermentation duration and quantification of moisture content & pH of cocoa beans in real-time usage.