Use of NIR Spectroscopy Combined with Recognition Methods for the Identification of Black Teas from Different Regions

2012 ◽  
Vol 503-504 ◽  
pp. 1601-1604 ◽  
Author(s):  
Jing Ming Ning ◽  
Sheng Peng Wang ◽  
Zheng Zhu Zhang ◽  
Xiao Chun Wan
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Black Tea ◽  
Rapid Identification ◽  
K Nearest Neighbor ◽  
Identification Rate

Near-infrared (NIR) spectroscopy, combined with pattern recognition, was applied in this study for the rapid identification of Black tea from different origins.The K-Nearest Neighbor model recognition method was used for the establishment of a tea origin recognition model, which involved optimization of the principal component factors (PCs) and the identification rate using a cross-validation method. The experimental results showed that, after standard normal variant spectral preprocessing, an optimized model was obtained when the PCs were equal to three, with the cross-validation recognition rate and the predicted recognition rate reaching 98.1% and 93.3%, respectively.

scholarly journals Screening of transgenic maize using near infrared spectroscopy and chemometric techniques

2018 ◽  
Vol 16 (2) ◽  
pp. e0203 ◽  
Author(s):  
Xuping Feng ◽  
Haijun Yin ◽  
Chu Zhang ◽  
Cheng Peng ◽  
Yong He
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Low Cost ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Transgenic Maize ◽  
K Nearest Neighbor ◽  
Full Spectrum ◽  
Maize Plants ◽  
Learning Machine

The applicability of near infrared (NIR) spectroscopy combined with chemometrics was examined to develop fast, low-cost and non-destructive spectroscopic methods for classification of transgenic maize plants. The transgenic maize plants containing both cry1Ab/cry2Aj-G10evo proteins and their non-transgenic parent were measured in the NIR diffuse reflectance mode with the spectral range of 700–1900 nm. Three variable selection algorithms, including weighted regression coefficients, principal component analysis -loadings and second derivatives were used to extract sensitive wavelengths that contributed the most discrimination information for these genotypes. Five classification methods, including K-nearest neighbor, Soft Independent Modeling of Class Analogy, Naive Bayes Classifier, Extreme Learning Machine (ELM) and Radial Basis Function Neural Network were used to build discrimination models based on the preprocessed full spectra and sensitive wavelengths. The results demonstrated that ELM had the best performance of all methods, even though the model’s recognition ability decreased as the variables in the training of neural networks were reduced by using only the sensitive wavelengths. The ELM model calculated on the calibration set showed classification rates of 100% based on the full spectrum and 90.83% based on sensitive wavelengths. The NIR spectroscopy combined with chemometrics offers a powerful tool for evaluating large number of samples from maize hybrid performance trials and breeding programs.

Rapid identification of plant- and chemical-dyed cotton fabrics using the near-infrared technique

2020 ◽  
Vol 90 (19-20) ◽  
pp. 2275-2283
Author(s):  
Mingxia Li ◽  
Guangting Han ◽  
Wei Jiang ◽  
Chengfeng Zhou ◽  
Yuanming Zhang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Recognition Rate ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Rapid Identification ◽  
Cotton Fabrics ◽  
Calibration Model ◽  
Pattern Recognition Methods ◽  
Validation Set

Plant dye is a promising dyestuff to be used in textiles due to its unique environmental compatibility. However, currently there is no effective method for the identification of plant-dyed and chemical-dyed textiles. In this study, near-infrared (NIR) spectroscopy combined with three kinds of pattern recognition methods, namely soft independent modeling of class analogy (SIMCA), partial least squares (PLS) regression and principal component regression (PCR), were applied to identify cotton fabrics dyed with plant and chemical dyes. A total of 336 plant dye and chemical dye dyed cotton fabrics were prepared and the NIR spectra were collected; 267 samples were used as the calibration set, while the remaining 69 samples were used as the validation set. After pretreatment with the Savitzky–Golay first derivative, the calibration model was constructed. In the SIMCA model, the correct recognition rate values of the calibration and prediction sets were 100% and 98.55%, respectively. The PLS model showed that the number of principal components (PCs) and the correlation coefficient ( R2) were 8 and 0.9978, respectively, and the results of PCR were PC = 10, R2 = 0.9937. Both methods were satisfactory for the predicted results. The overall results indicated that NIR spectroscopy could be used for rapid and nondestructive identification of plant-dyed cotton fabrics and chemical-dyed cotton fabrics.

Two-parameter KNN algorithm and its application in recognition of brand rice

2021 ◽  
pp. 1-7
Author(s):  
Zhu Siyu ◽  
He Chongnan ◽  
Song Mingjuan ◽  
Li Linna
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Pattern Class ◽  
Fisher Discriminant ◽  
Two Parameter

In response to the frequent counterfeiting of Wuchang rice in the market, an effective method to identify brand rice is proposed. Taking the near-infrared spectroscopy data of a total of 373 grains of rice from the four origins (Wuchang, Shangzhi, Yanshou, and Fangzheng) as the observations, kernel principal component analysis(KPCA) was employed to reduce the dimensionality, and Fisher discriminant analysis(FDA) and k-nearest neighbor algorithm (KNN) were used to identify brand rice respectively. The effects of the two recognition methods are very good, and that of KNN is relatively better. Howerver the shortcomings of KNN are obvious. For instance, it has only one test dimension and its test of samples is not delicate enough. In order to further improve the recognition accuracy, fuzzy k-nearest neighbor set is defined and fuzzy probability theory is employed to get a new recognition method –Two-Parameter KNN discrimination method. Compared with KNN algorithm, this method increases the examination dimension. It not only examines the proportion of the number of samples in each pattern class in the k-nearest neighbor set, but also examines the degree of similarity between the center of each pattern class and the sample to be identified. Therefore, the recognition process is more delicate and the recognition accuracy is higher. In the identification of brand rice, the discriminant accuracy of Two-Parameter KNN algorithm is significantly higher than that of FDA and that of KNN algorithm.

scholarly journals Analisis Metode Pengenalan Wajah Two Dimensial Principal Component Analysis (2DPCA) dan Kernel Fisher Discriminant Analysis Menggunakan Klasifikasi KNN (K- Nearest Neighbor)

2020 ◽  
Vol 2 (2) ◽  
pp. 29-38
Author(s):  
Abdur Rohman Harits Martawireja ◽  
Hilman Mujahid Purnama ◽  
Atika Nur Rahmawati
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis

Pengenalan wajah manusia (face recognition) merupakan salah satu bidang penelitian yang penting dan belakangan ini banyak aplikasi yang menerapkannya, baik di bidang komersil ataupun di bidang penegakan hukum. Pengenalan wajah merupakan sebuah sistem yang berfungsikan untuk mengidentifikasi berdasarkan ciri-ciri dari wajah seseorang berbasis biometrik yang memiliki keakuratan tinggi. Pengenalan wajah dapat diterapkan pada sistem keamanan. Banyak metode yang dapat digunakan dalam aplikasi pengenalan wajah untuk keamanan sistem, namun pada artikel ini akan membahas tentang dua metode yaitu Two Dimensial Principal Component Analysis dan Kernel Fisher Discriminant Analysis dengan metode klasifikasi menggunakan K-Nearest Neigbor. Kedua metode ini diuji menggunakan metode cross validation. Hasil dari penelitian terdahulu terbukti bahwa sistem pengenalan wajah metode Two Dimensial Principal Component Analysis dengan 5-folds cross validation menghasilkan akurasi sebesar 88,73%, sedangkan dengan 2-folds validation akurasi yang dihasilkan sebesar 89,25%. Dan pengujian metode Kernel Fisher Discriminant dengan 2-folds cross validation menghasilkan akurasi rata rata sebesar 83,10%.

Classification of fish species from different ecosystems using the near infrared diffuse reflectance spectra of otoliths

2020 ◽  
Vol 28 (4) ◽  
pp. 224-235
Author(s):  
Irina M Benson ◽  
Beverly K Barnett ◽  
Thomas E Helser
Keyword(s):  
Discriminant Analysis ◽  
Spatial Variability ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Otolith Chemistry ◽  
K Nearest Neighbor ◽  
Fisheries Science

Applications of Fourier transform near infrared (FT-NIR) spectroscopy in fisheries science are currently limited. This current analysis of otolith spectral data demonstrate the potential applicability of FT-NIR spectroscopy to otolith chemistry and spatial variability in fisheries science. The objective of this study was to examine the use of NIR spectroscopy as a tool to differentiate among marine fishes in four large marine ecosystems. We examined otoliths from 13 different species, with three of these species coming from different regions. Principal component analysis described the main directions along which the specimens were separated. The separation of species and their ecosystems may suggest interactions between fish phylogeny, ontogeny, and environmental conditions that can be evaluated using NIR spectroscopy. In order to discriminate spectra across ecosystems and species, four supervised classification model techniques were utilized: soft independent modelling of class analogies, support vector machine discriminant analysis, partial least squares discriminant analysis, and k-nearest neighbor analysis (KNN). This study showed that the best performing model to classify combined ecosystems, all four ecosystems, and species was the KNN model, which had an overall accuracy rate of 99.9%, 97.6%, and 91.5%, respectively. Results from this study suggest that further investigations are needed to determine applications of NIR spectroscopy to otolith chemistry and spatial variability.

scholarly journals Feasibility Study of the Use of Handheld NIR Spectrometer for Simultaneous Authentication and Quantification of Quality Parameters in Intact Pineapple Fruits

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Charles L. Y. Amuah ◽  
Ernest Teye ◽  
Francis Padi Lamptey ◽  
Kwasi Nyandey ◽  
Jerry Opoku-Ansah ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Multivariate Calibration ◽  
Principal Component ◽  
Quality Parameters ◽  
Soluble Solids ◽  
Least Squares Regression ◽  
Total Soluble Solids ◽  
Linear Discriminant ◽  
Identification Rate

The potential of predicting maturity using total soluble solids (TSS) and identifying organic from inorganic pineapple fruits based on near-infrared (NIR) spectra fingerprints would be beneficial to farmers and consumers alike. In this study, a portable NIR spectrometer and chemometric techniques were combined to simultaneously identify organically produced pineapple fruits from conventionally produced ones (thus organic and inorganic) and also predict total soluble solids. A total of 90 intact pineapple fruits were scanned with the NIR spectrometer while a digital refractometer was used to measure TSS from extracted pineapple juice. After attempting several preprocessing techniques, multivariate calibration models were built using principal component analysis (PCA), K-nearest neighbor (KNN), and linear discriminant analysis (LDA) to identify the classes (organic and conventional pineapple fruits) while partial least squares regression (PLSR) method was used to determine TSS of the fruits. Among the identification techniques, the MSC-PCA-LDA model accurately identified organic from conventionally produced fruits at 100% identification rate. For quantification of TSS, the MSC-PLSR model gave Rp = 0.851 and RMSEC = 0.950 °Brix, and Rc = 0.854 and RMSEP = 0.842 °Brix at 5 principal components in the calibration set and prediction set, respectively. The results generally indicated that portable NIR spectrometer coupled with the appropriate chemometric tools could be employed for rapid nondestructive examination of pineapple quality and also to detect pineapple fraud due to mislabeling of conventionally produced fruits as organic ones. This would be helpful to farmers, consumers, and quality control officers.

scholarly journals Authenticity Detection of Black Rice by Near-Infrared Spectroscopy and Support Vector Data Description

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Chao Tan ◽  
Zan Lin
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
K Nearest Neighbor ◽  
Black Rice ◽  
Target Class ◽  
Data Description

Black rice is an important rice species in Southeast Asia. It is a common phenomenon to pass low-priced black rice off as high-priced ones for economic benefit, especially in some remote towns. There is increasing need for the development of fast, easy-to-use, and low-cost analytical methods for authenticity detection. The feasibility to utilize near-infrared (NIR) spectroscopy and support vector data description (SVDD) for such a goal is explored. Principal component analysis (PCA) is used for exploratory analysis and feature extraction. Another two data description methods, i.e., k-nearest neighbor data description (KNNDD) and GAUSS method, are used as the reference. A total of 142 samples from three brands were collected for spectral analysis. Each time, the samples of a brand serve as the target class whereas other samples serve as the outlier class. Based on both the first two principal components (PCs) and original variables, three types of data descriptions were constructed. On average, the optimized SVDD model achieves acceptable performance, i.e., a specificity of 100% and a sensitivity of 94.2% on the independent test set with tight boundary. It indicates that SVDD combined with NIR is feasible and effective for authenticity detection of black rice.

scholarly journals Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yinglin Yang ◽  
Xin Zhang ◽  
Jianwei Yin ◽  
Xiangyang Yu
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Diffuse Reflectance Spectrum ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Site Classification ◽  
Classification Models ◽  
K Nearest Neighbor

The classification of plastic waste before recycling is of great significance to achieve effective recycling. In order to achieve rapid, nondestructive, and on-site detection, a portable near-infrared spectrometer was used in this study to obtain the diffuse reflectance spectrum for both standard and commercial plastics made by ABS, PC, PE, PET, PP, PS, and PVC. After applying a series of pretreatments, the principal component analysis (PCA) was used to analyze the cluster trend. K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural network (BPNN) classification models were developed and evaluated, respectively. The result showed that different plastics could be well separated in top three principal components space after pretreatment, and the classification models performed excellent classification results and high generalization capability. This study indicated that the portable NIR spectrometer, integrated with chemometrics, could achieve excellent performance and has great potential in the field of commercial plastic identification.

scholarly journals Rapid identification of wood species by near-infrared spatially resolved spectroscopy (NIR-SRS) based on hyperspectral imaging (HSI)

Holzforschung ◽  
2019 ◽  
Vol 73 (4) ◽  
pp. 323-330 ◽  
Author(s):  
Te Ma ◽  
Tetsuya Inagaki ◽  
Mayuka Ban ◽  
Satoru Tsuchikawa
Keyword(s):  
Light Scattering ◽  
Hyperspectral Imaging ◽  
Wood Species ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Rapid Identification ◽  
Identification Accuracy ◽  
Image Histogram ◽  
Halogen Light

AbstractConventional near-infrared (NIR) spectroscopy has shown its potential to separate wood species nondestructively based on the aggregate effect of light absorption and scattering values. However, wood has an aligned microstructure, and there is a large refractive index (RI) mismatch between the wood cell wall substance (n≈1.55) and the cell lumen (air≈1.0, water≈1.33). Light scattering is dominant over absorption$({\mu '_s} \gg {\mu _a})$in wood, and this fact can be utilized for complex classification purposes. In this study, an NIR hyperspectral imaging (HSI) camera combined with one focused halogen light source (Ø 1 mm) was designed to evaluate the light scattering patterns of five softwood (SW) and 10 hardwood (HW) species in the wavelength range from 1002 to 2130 nm. Several parameters were combined to improve the data quality, such as image histogram plots of defined spaced bins (associated with diffuse reflectance values of light), variance calculation on the frequency (the number of pixels in each bin) of each histogram and the principal component analysis (PCA) of all the variance values at each wavelength. The identification accuracy of the quadratic discriminant analysis (QDA) under the five-fold cross-validation method was 94.1%, based on the first three principal component (PC) scores.

scholarly journals Studi Komparasi Algoritma Klasifikasi Mental Workload Berdasarkan Sinyal EEG

2020 ◽  
Vol 3 (2) ◽  
pp. 133-143
Author(s):  
Dessy Kusumaningrum ◽  
Elly Matul Imah
Keyword(s):  
Random Forest ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Mental Workload ◽  
Principal Component ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
K Nearest Neighbor ◽  
Electroencephalogram Eeg ◽  
Fold Cross Validation

Kondisi psikologis dan fisik manusia dapat memengaruhi proses berpikir. Apabila kondisi individu mengalami kelelahan, maka dapat memengaruhi penurunan tingkat produktivitas maupun penurunan proses berpikir yang menyebabkan timbulnya mental workload. Workload yang dimiliki harus seimbang terhadap kemampuan dan keterbatasan yang dimiliki. Mental workload yang berlebih berdampak buruk bagi individu karena menimbulkan penurunan produktivitas kerja. Perangkat khusus yang dapat digunakan untuk mengetahui tingkat mental workload seorang individu adalah Electroencephalogram (EEG). EEG adalah perangkat khusus yang digunakan untuk mengukur sinyal potensi listrik dari otak. Dataset yang digunakan dalam penelitian ini adalah STEW: Simultaneous Task EEG Dataset dengan 45 subjek. Dalam penelitian ini, telah dilakukan studi komparasi algoritma Random Forest, K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), dan Support Vector Machine (SVM) untuk klasifikasi mental workload berdasarkan sinyal EEG. Studi dilakukan untuk menentukan algoritma terbaik dalam klasifikasi dilihat dari segi nilai akurasi dan penggunaan memori saat proses klasifikasi. Dataset telah melalui beberapa tahapan, diantaranya pra-pemrosesan data, ekstraksi fitur, dan proses klasifikasi. Pra-pemrosesan data menerapkan pembagian data menjadi beberapa chunk. Untuk mendapatkan ciri dalam ekstraksi fitur, diterapkan metode Principal Component Analysis (PCA). Pada proses klasifikasi menggunakan pendekatan k-fold cross validation. Hasil studi penelitian ini adalah algoritma terbaik dari sisi akurasi adalah algoritma KNN, algoritma terbaik dari sisi waktu pembuatan model adalah algoritma Random Forest, serta algoritma terbaik dari sisi penggunaan memori adalah algoritma MLP.

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