Nondestructive identification of tea (Camellia sinensis L.) varieties using FT-NIR spectroscopy and pattern recognition

Due to more and more tea varieties in the current tea market, rapid and accurate identification of tea (<I>Camellia sinensis</I> L.) varieties is crucial to the tea quality control. Fourier Transform Near-Infrared (FT-NIR) spectroscopy coupled with the pattern recognition was used to identify individual tea varieties as a rapid and non-invasive analytical tool in this work. Seven varieties of Chinese tea were studied in the experiment. Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN) were compared to construct the identification models based on Principal Component Analysis (PCA). The number of principal components factors (PCs) was optimised in the constructing model. The experimental results showed that the performance of ANN model was better than LDA models. The optimal ANN model was achieved when four PCs were used, identification rates being all 100% in the training and prediction sets. The overall results demonstrated that FT-NIR spectroscopy technology with ANN pattern recognition method can be successfully applied as a rapid method to identify tea varieties.

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Near Infrared Spectroscopy Based on Supervised Pattern Recognition Methods for Rapid Identification of Adulterated Edible Gelatin

Journal of Spectroscopy ◽

10.1155/2018/7652592 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Hao Zhang ◽

Haifeng Sun ◽

Ling Wang ◽

Shun Wang ◽

Wei Zhang ◽

...

Keyword(s):

Pattern Recognition ◽

Near Infrared ◽

Scatter Correction ◽

Recognition Rate ◽

Nir Spectroscopy ◽

Principal Component ◽

Support Vector ◽

Linear Discriminant ◽

Pattern Recognition Methods ◽

Gelatin Gels

The aim of this work is to identify the adulteration of edible gelatin using near-infrared (NIR) spectroscopy combined with supervised pattern recognition methods. The spectral data obtained from a total of 144 samples consisting of six kinds of adulterated gelatin gels with different mixture ratios were processed with multiplicative scatter correction (MSC), Savitzky–Golay (SG) smoothing, and min-max normalization. Principal component analysis (PCA) was first carried out for spectral analysis, while the six gelatin categories could not be clearly distinguished. Further, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA), backpropagation neural network (BPNN), and support vector machine (SVM) were introduced to establish discrimination models for identifying the adulterated gelatin gels, which gave a total correct recognition rate of 97.44%, 100%, 97.44%, and 100%, respectively. For the SIMCA model with significant level α = 0.05, sample overlapping clustering appeared; thus, the SVM model presents the best recognition ability among these four discrimination models for the classification of edible gelatin adulteration. The results demonstrate that NIR spectroscopy combined with unsupervised pattern recognition methods can quickly and accurately identify edible gelatin with different adulteration levels, providing a new possibility for the detection of industrial gelatin illegally added into food products.

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A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033520987315 ◽

2021 ◽

pp. 096703352098731

Author(s):

Adenilton C da Silva ◽

Lívia PD Ribeiro ◽

Ruth MB Vidal ◽

Wladiana O Matos ◽

Gisele S Lopes

Keyword(s):

Quality Control ◽

Discriminant Analysis ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Chemical Components ◽

Linear Discriminant ◽

Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

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Tracing the Geographical Origin of Durum Wheat by FT-NIR Spectroscopy

Foods ◽

10.3390/foods8100450 ◽

2019 ◽

Vol 8 (10) ◽

pp. 450 ◽

Cited By ~ 4

Author(s):

Annalisa De Girolamo ◽

Marina Cortese ◽

Salvatore Cervellieri ◽

Vincenzo Lippolis ◽

Michelangelo Pascale ◽

...

Keyword(s):

Durum Wheat ◽

Near Infrared ◽

Southern Italy ◽

Geographical Origin ◽

Nir Spectroscopy ◽

Principal Component ◽

Classification Model ◽

North Central ◽

Linear Discriminant ◽

The World

Fourier transform near infrared (FT-NIR) spectroscopy, in combination with principal component-linear discriminant analysis (PC-LDA), was used for tracing the geographical origin of durum wheat samples. The classification model PC-LDA was applied to discriminate durum wheat samples originating from Northern, Central, and Southern Italy (n = 181), and to differentiate Italian durum wheat samples from those cultivated in other countries across the world (n = 134). Developed models were validated on a separated set of wheat samples. Different pre-treatments of spectral data and different spectral regions were selected and compared in terms of overall discrimination (OD) rates obtained in validation. The LDA models were able to correctly discriminate durum Italian wheat samples according to their geographical origin (i.e., North, Central, and South) with OD rates of up of 96.7%. Better results were obtained when LDA models were applied to the discrimination of Italian durum wheat samples from those originating from other countries across the world, having OD rates of up to 100%. The excellent results obtained herein clearly indicate the potential of FT-NIR spectroscopy to be used for the discrimination of durum wheat samples according to their geographical origin.

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Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools

Foods ◽

10.3390/foods9111551 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1551

Author(s):

Annalisa De Girolamo ◽

Salvatore Cervellieri ◽

Erminia Mancini ◽

Michelangelo Pascale ◽

Antonio Francesco Logrieco ◽

...

Keyword(s):

Durum Wheat ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Linear Discriminant ◽

Production And Consumption ◽

High Prediction ◽

Validation Set ◽

Performance Results ◽

Prediction Capability

Italy is the country with the largest durum wheat pasta production and consumption. The mandatory labelling for pasta indicating the country of origin of wheat has made consumers more aware about the consumed pasta products and is influencing their choice towards 100% Italian wheat pasta. This aspect highlights the need to promote the use of domestic wheat as well as to develop rapid methodologies for the authentication of pasta. A rapid, inexpensive, and easy-to-use method based on infrared spectroscopy was developed and validated for authenticating pasta made with 100% Italian durum wheat. The study was conducted on pasta marketed in Italy and made with durum wheat cultivated in Italy (n = 176 samples) and on pasta made with mixtures of wheat cultivated in Italy and/or abroad (n = 185 samples). Pasta samples were analyzed by Fourier transform-near infrared (FT-NIR) spectroscopy coupled with supervised classification models. The good performance results of the validation set (sensitivity of 95%, specificity and accuracy of 94%) obtained using principal component-linear discriminant analysis (PC-LDA) clearly demonstrated the high prediction capability of this method and its suitability for authenticating 100% Italian durum wheat pasta. This output is of great interest for both producers of Italian pasta pointing toward authentication purposes of their products and consumer associations aimed to preserve and promote the typicity of Italian products.

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Near-Infrared Spectroscopy as a Diagnostic Tool for Distinguishing between Normal and Malignant Colorectal Tissues

BioMed Research International ◽

10.1155/2015/472197 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Hui Chen ◽

Zan Lin ◽

Lin Mo ◽

Tong Wu ◽

Chao Tan

Keyword(s):

Discriminant Analysis ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Projection Algorithm ◽

Diagnostic Model ◽

Full Spectrum ◽

Analytical Strategy ◽

Linear Discriminant ◽

Parsimonious Model

Cancer diagnosis is one of the most important tasks of biomedical research and has become the main objective of medical investigations. The present paper proposed an analytical strategy for distinguishing between normal and malignant colorectal tissues by combining the use of near-infrared (NIR) spectroscopy with chemometrics. The successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to seek a reduced subset of variables/wavenumbers and build a diagnostic model of LDA. For comparison, the partial least squares-discriminant analysis (PLS-DA) based on full-spectrum classification was also used as the reference. Principal component analysis (PCA) was used for a preliminary analysis. A total of 186 spectra from 20 patients with partial colorectal resection were collected and divided into three subsets for training, optimizing, and testing the model. The results showed that, compared to PLS-DA, SPA-LDA provided more parsimonious model using only three wavenumbers/variables (4065, 4173, and 5758 cm−1) to achieve the sensitivity of 84.6%, 92.3%, and 92.3% for the training, validation, and test sets, respectively, and the specificity of 100% for each subset. It indicated that the combination of NIR spectroscopy and SPA-LDA algorithm can serve as a potential tool for distinguishing between normal and malignant colorectal tissues.

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Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545818500050 ◽

2018 ◽

Vol 11 (02) ◽

pp. 1850005 ◽

Cited By ~ 2

Author(s):

Lijun Yao ◽

Weiqun Xu ◽

Tao Pan ◽

Jiemei Chen

Keyword(s):

Discriminant Analysis ◽

Near Infrared ◽

Large Population ◽

Correlation Coefficients ◽

Nir Spectroscopy ◽

Principal Component ◽

Moving Window ◽

Linear Discriminant ◽

Transgenic Sugarcane ◽

Polynomial Formula

The moving-window bis-correlation coefficients (MW-BiCC) was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and [Formula: see text]-thalassemia with visible and near-infrared (Vis–NIR) spectroscopy. The well-performed moving-window principal component analysis linear discriminant analysis (MW-PCA–LDA) was also conducted for comparison. A total of 306 transgenic (positive) and 150 nontransgenic (negative) leave samples of sugarcane were collected and divided to calibration, prediction, and validation. The diffuse reflection spectra were corrected using Savitzky–Golay (SG) smoothing with first-order derivative ([Formula: see text]), third-degree polynomial ([Formula: see text]) and 25 smoothing points ([Formula: see text]). The selected waveband was 736–1054[Formula: see text]nm with MW-BiCC, and the positive and negative validation recognition rates ([Formula: see text]_REC[Formula: see text], [Formula: see text]_REC[Formula: see text] were 100%, 98.0%, which achieved the same effect as MW-PCA–LDA. Another example, the 93 [Formula: see text]-thalassemia (positive) and 148 nonthalassemia (negative) of human hemolytic samples were collected. The transmission spectra were corrected using SG smoothing with [Formula: see text], [Formula: see text] and [Formula: see text]. Using MW-BiCC, many best wavebands were selected (e.g., 1116–1146, 1794–1848 and 2284–2342[Formula: see text]nm). The [Formula: see text]_REC[Formula: see text] and [Formula: see text]_REC[Formula: see text] were both 100%, which achieved the same effect as MW-PCA–LDA. Importantly, the BiCC only required calculating correlation coefficients between the spectrum of prediction sample and the average spectra of two types of calibration samples. Thus, BiCC was very simple in algorithm, and expected to obtain more applications. The results first confirmed the feasibility of distinguishing [Formula: see text]-thalassemia and normal control samples by NIR spectroscopy, and provided a promising simple tool for large population thalassemia screening.

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Nondestructive Discrimination of Grape Seed Varieties Using UV-VIS-NIR Spectroscopy and Chemometrics

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.236-237.89 ◽

2012 ◽

Vol 236-237 ◽

pp. 89-94 ◽

Cited By ~ 4

Author(s):

Hai Qing Yang ◽

Wei Qiang Luo ◽

Wen Jing Wang

Keyword(s):

Near Infrared ◽

Full Range ◽

Ultra Violet ◽

Nir Spectroscopy ◽

Principal Component ◽

Calibration Method ◽

Grape Seed ◽

Support Vector ◽

Grape Seeds ◽

Linear Discriminant

Classification of grape seed species is a useful tool to obtain seeds with desired quality traits. This study aimed at rapidly and nondestructively discriminating four varieties of grape seeds using ultra violet, visible and near infrared (UV-VIS-NIR) spectroscopy with wavelength range of 2101100 nm. A hundred twenty grape seed samples were divided for calibration (n=80) and validation (n=40). The spectra were subjected to a principal component analysis (PCA) with the leading 10 principal components (PCs) used to build calibration models. The obtained PCs were treated by linear discriminant analysis (LDA), artificial neural network (ANN) and support vector machine (SVM) to build various discrimination models. Validation results showed that the PC-LDA model developed for the full range of UV-VIS-NIR achieved better performance than those developed for partial wavelengths, i.e. UV, VIS, NIR, UV-VIS, and VIS-NIR. The PC-LDA model with 8 PCs achieved best performance with 100% discrimination accuracy. This experiment suggests that the UV-VIS-NIR spectroscopy coupled with PC-LDA calibration method is promising for the nondestructive discrimination of grape seed varieties.

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The Use of Visible and near Infrared Spectroscopy to Classify the Floral Origin of Honey Samples Produced in Uruguay

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.458 ◽

2005 ◽

Vol 13 (2) ◽

pp. 63-68 ◽

Cited By ~ 26

Author(s):

E. Corbella ◽

D. Cozzolino

Keyword(s):

Principal Component Analysis ◽

Infrared Spectroscopy ◽

Discriminant Analysis ◽

Sample Preparation ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Classification Models ◽

Linear Discriminant

This study reports the use of visible (vis) and near infrared (NIR) spectroscopy as a tool to classify honey samples from Uruguay, according to their floral origin. Classification models were developed using principal component analysis, discriminant partial least squares (DPLS) regression and linear discriminant analysis (LDA). Honey samples ( n = 50) from two floral origins, namely Eucalyptus spp. and pasture, were split randomly into even calibration ( n = 25) and validation sets ( n = 25). Both LDA and DPLS models correctly classified, on average, more than 75% of the honey samples belonging to pasture and more than 85% of the honey samples belonging to Eucalyptus spp. These results showed that vis-NIR might be a suitable and alternative method that can easily be implemented by both the industry and retailers to classify samples according their floral origin. Vis-NIR analysis requires little sample preparation and is rapid. However, the relatively limited number of samples involved in the present work led us to be cautious in terms of extrapolating the results of this work to other floral types.

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Textile Fiber Identification Using Near-Infrared Spectroscopy and Pattern Recognition

Autex Research Journal ◽

10.1515/aut-2018-0055 ◽

2019 ◽

Vol 19 (2) ◽

pp. 201-209 ◽

Cited By ~ 4

Author(s):

Jinfeng Zhou ◽

Lingjie Yu ◽

Qian Ding ◽

Rongwu Wang

Keyword(s):

Pattern Recognition ◽

Near Infrared ◽

Raw Materials ◽

Recognition Rate ◽

Nir Spectroscopy ◽

Principal Component ◽

Rejection Rate ◽

Chemical Compositions ◽

Target Fibers ◽

Materials Used

Abstract Fibers are raw materials used for manufacturing yarns and fabrics, and their properties are closely related to the performances of their derivatives. It is indispensable to implement fiber identification in analyzing textile raw materials. In this paper, seven common fibers, including cotton, tencel, wool, cashmere, polyethylene terephthalate (PET), polylactic acid (PLA), and polypropylene (PP), were prepared. After analyzing the merits and demerits of the current methods used to identify fibers, near-infrared (NIR) spectroscopy was used owing to its significant superiorities, the foremost of which is it can capture the tiny information differences in chemical compositions and morphological features to display the characteristic spectral curve of each fiber. First, the fibers’ spectra were collected, and then, the relationships between the vibrations of characteristic chemical groups and the corresponding wavelengths were researched to organize a spectral information library that would be beneficial to achieve quick identification and classification. Finally, to achieve intelligent detection, pattern recognition approaches, including principal component analysis (PCA) (used to extract information of interest), soft independent modeling of class analogy (SIMCA), and linear discrimination analysis (LDA) (defined using two classifiers), assisted in accomplishing fiber identification. The experimental results – obtained by combining PCA and SIMCA – displayed that five of seven target fibers, namely, cotton, tencel, PP, PLA, and PET, were distributed with 100% recognition rate and 100% rejection rate, but wool and cashmere fibers yielded confusing results and led to relatively low recognition rate because of the high proportion of similarities between these two fibers. Therefore, the six spectral bands of interest unique to wool and cashmere fibers were selected, and the absorbance intensities were imported into the classifier LDA, where wool and cashmere were group-distributed in two different regions with 100% recognition rate. Consequently, the seven target fibers were accurately and quickly distinguished by the NIR method to guide the fiber identification of textile materials.

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Research on the Variety Discrimination of Apple Using a Hybrid Possibilistic Clustering

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.710.768 ◽

2013 ◽

Vol 710 ◽

pp. 768-771 ◽

Cited By ~ 1

Author(s):

Xiao Hong Wu ◽

Tong Xiang Cai ◽

Bin Wu ◽

Jun Sun

Keyword(s):

Clustering Algorithm ◽

Near Infrared ◽

Learning Algorithm ◽

Nir Spectroscopy ◽

Principal Component ◽

Simple Computation ◽

Near Infrared Reflectance ◽

Linear Discriminant ◽

Possibilistic Clustering ◽

Discriminant Information

Near infrared reflectance (NIR) spectroscopy has been used to obtain NIR spectra of two varieties of apple samples. The dimensionality of NIR spectra was reduced by principal component analysis (PCA), and discriminant information was extracted by linear discriminant analysis (LDA). Last, a hybrid possibilistic clustering algorithm (HPCA) was utilized as classifier to discriminate the apple samples of different varieties. HPCA integrates possibilistic clustering algorithm (PCA) and improved possibilistic c-means (IPCM) clustering algorithm, and produces not only the membership values but also typicality values by simple computation of the sample co-variance. Experimental results showed that HPCA, as an unsupervised learning algorithm, could quickly and easily discriminate the apple varieties.

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