scholarly journals Quality Degradation of Chinese White Lotus Seeds Caused by Dampening during Processing and Storage: Rapid and Nondestructive Discrimination Using Near-Infrared Spectroscopy

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Lu Xu ◽  
Hai-Yan Fu ◽  
Chen-Bo Cai ◽  
Yuan-Bin She
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Standard Normal Variate ◽  
Chinese White ◽  
Standard Normal ◽  
Processing And Storage ◽  
Sensitivity Specificity ◽  
Spectral Ranges ◽  
And Storage

Dampening during processing or storage can largely influence the quality of white lotus seeds (WLS). This paper investigated the feasibility of using near-infrared (NIR) spectroscopy and chemometrics for rapid and nondestructive discrimination of the dampened WLS. Regular (n=167) and dampened (n=118) WLS objects were collected from five main producing areas and NIR reflectance spectra (4000–12000 cm−1) were measured for bare kernels. The influence of spectral preprocessing methods, including smoothing, taking second-order derivatives (D2), and standard normal variate (SNV), on partial least squares discrimination analysis (PLSDA) was compared to select the optimal data preprocessing method. A moving-window strategy was combined with PLSDA (MWPLSDA) to select the most informative wavelength intervals for classification. Based on the selected spectral ranges, the sensitivity, specificity, and accuracy were 0.927, 0.950, and 0.937 for SNV-MWPLSDA, respectively.

Identification of geographical origin of TPFD based on handheld NIR spectroscopy and PLSDA

NIR news ◽  
2020 ◽  
Vol 31 (5-6) ◽  
pp. 25-29
Author(s):  
Rita-Cindy Aye-Ayire Sedjoah ◽  
Bangxing Han ◽  
Hui Yan
Keyword(s):  
Quality Control ◽  
Near Infrared ◽  
Geographical Origin ◽  
Nir Spectroscopy ◽  
Analysis Model ◽  
First Order ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer

The present study is focused on the identification of geographical origin (Zhejiang, Yunnan and Anhui, China) of Dendrobium officinale’s dried stem called Tiepi fengdou by mean of the handheld near-infrared spectrometer. Raw data were preprocessed to reduce unwanted spectral variations by the first-order derivative followed by standard normal variate transformation, and partial least squares discriminant analysis model was developed for calibration. The results showed that more than 90% of the origins were identified. Therefore, it is possible to classify the geographical origin of Tiepi fengdou by the use of the handheld near-infrared spectrometer for effective quality control.

scholarly journals Automatic and Rapid Discrimination of Cotton Genotypes by Near Infrared Spectroscopy and Chemometrics

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Hai-Feng Cui ◽  
Zi-Hong Ye ◽  
Lu Xu ◽  
Xian-Shu Fu ◽  
Cui-Wen Fan ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Standard Normal Variate ◽  
Pattern Recognition Methods ◽  
Standard Normal ◽  
Cotton Genotypes ◽  
Rapid Discrimination

This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n=120) and leaves (n=123) were measured in the range of 4000–12000 cm−1. A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal component analysis (rPCA) was applied to each separate sample group to detect and exclude outliers. The influence of different data preprocessing methods on model prediction performance was also investigated. The results demonstrate that rPCA can effectively detect outliers and maintain the efficiency of discriminant analysis. Moreover, the classification accuracy can be significantly improved by second-order derivative and standard normal variate (SNV). The best partial least squares discriminant analysis (PLSDA) models obtained total classification accuracy of 100% and 97.6% for seeds and leaves, respectively.

scholarly journals The Feasibility of Using Near-Infrared Spectroscopy and Chemometrics for Untargeted Detection of Protein Adulteration in Yogurt: Removing Unwanted Variations in Pure Yogurt

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Lu Xu ◽  
Si-Min Yan ◽  
Chen-Bo Cai ◽  
Zhen-Ji Wang ◽  
Xiao-Ping Yu
Keyword(s):  
Soy Protein ◽  
Near Infrared ◽  
Pure Water ◽  
Nir Spectroscopy ◽  
Local Market ◽  
Fresh Milk ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Modelling Techniques ◽  
Different Levels

Untargeted detection of protein adulteration in Chinese yogurt was performed using near-infrared (NIR) spectroscopy and chemometrics class modelling techniques. sixty yogurt samples were prepared with pure and fresh milk from local market, and 197 adulterated yogurt samples were prepared by blending the pure yogurt objects with different levels of edible gelatin, industrial gelatin, and soy protein powder, which have been frequently used for yogurt adulteration. A recently proposed one-class partial least squares (OCPLS) model was used to model the NIR spectra of pure yogurt objects and analyze those of future objects. To improve the raw spectra, orthogonal projection (OP) of raw spectra onto the spectrum of pure water and standard normal variate (SNV) transformation were used to remove unwanted spectral variations. The best model was obtained with OP preprocessing with sensitivity of 0.900 and specificity of 0.949. Moreover, adulterations of yogurt with 1% (w/w) edible gelatin, 2% (w/w) industrial gelatin, and 2% (w/w) soy protein powder can be safely detected by the proposed method. This study demonstrates the potential of combining NIR spectroscopy and OCPLS as an untargeted detection tool for protein adulteration in yogurt.

scholarly journals Robust and Automated Internal Quality Grading of a Chinese Green Tea (Longjing) by Near-Infrared Spectroscopy and Chemometrics

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xian-Shu Fu ◽  
Lu Xu ◽  
Xiao-Ping Yu ◽  
Zi-Hong Ye ◽  
Hai-Feng Cui
Keyword(s):  
Quality Control ◽  
Green Tea ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Internal Quality Control ◽  
Internal Quality ◽  
Outlier Analysis ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Quality Grading

Near-infrared (NIR) spectroscopy and chemometric methods were applied to internal quality control of a Chinese green tea, Longjing, with Protected Geographical Indication (PGI). A total of 2745 authentic Longjing tea samples of three different grades were analyzed by NIR spectroscopy. To remove the influence of abnormal samples, The Stahel-Donoho estimate (SDE) of outlyingness was used for outlier analysis. Partial least squares discriminant analysis (PLSDA) was then used to classify the grades of tea based on NIR spectra. Different data preprocessing methods, including smoothing, taking second-order derivative (D2) spectra, and standard normal variate (SNV) transformation, were performed to reduce unwanted spectral variations in samples of the same grade before classification models were developed. The results demonstrate that smoothing, taking D2 spectra, and SNV can improve the performance of PLSDA models. With SNV spectra, the model sensitivity was 1.000, 0.955, and 0.924, and the model specificity was 0.979, 0.952, and 0.996 for samples of three grades, respectively. FT-NIR spectrometry and chemometrics can provide a robust and effective tool for rapid internal quality control of Longjing green tea.

A Chemometrics Approach for Distinguishing between Beers Using near Infrared Spectroscopy

2009 ◽  
Vol 17 (2) ◽  
pp. 69-76 ◽  
Author(s):  
Hua Li ◽  
Yutaka Takahashi ◽  
Masanori Kumagai ◽  
Kazuhiko Fujiwara ◽  
Ryoei Kikuchi ◽  
...  
Keyword(s):  
Quality Evaluation ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Total Polyphenol ◽  
Standard Normal Variate ◽  
Production Processes ◽  
Standard Normal ◽  
Akita Prefecture ◽  
Beer Production

Thirty-eight beers from different producing areas and/or makers were distinguished by principal component analysis (PCA) of the near infrared (NIR) spectra acquired by a portable NIR spectrometer. Classsification of Akita beers: beers locally produced in Akita prefecture, Japan, from other famous brand beers could be successfully performed, especially when the PCA was calculated on the standard normal variate (SNV) spectra. The classification equations use information related to water and CH2 absorption that reflected the differences in chemical com position of beers due to different production processes. In addition, the compositions of total polyphenol and total nitrogen were estimated from NIR spectra by multiple linear regression (MLR). This study showed that NIR spectroscopy is promising for beer quality evaluation, both for identifying multifarious beers including Akita beers using PCA and for rapid in-line quality control and inspection for beer production using the quantitative MLR analysis.

scholarly journals Application of calibrations to hyperspectral images of food grains: example for wheat falling number

2017 ◽  
Author(s):  
Nicola Caporaso ◽  
Martin Whitworth ◽  
Ian Fisk
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Least Squares Regression ◽  
Falling Number ◽  
Linear Discriminant ◽  
Standard Normal Variate ◽  
Average Spectrum ◽  
Standard Normal ◽  
Improved Performance

The presence of a few kernels with sprouting problems in a batch of wheat can result in enzymatic activity sufficient to compromise flour functionality and bread quality. This is commonly assessed using the Hagberg Falling Number (HFN) method, which is a batch analysis. Hyperspectral imaging (HSI) can provide analysis at the single grain level with potential for improved performance. The present paper deals with the development and application of calibrations obtained using an HSI system working in the near infrared (NIR) region (~900–2500 nm) and reference measurements of HFN. A partial least squares regression calibration has been built using 425 wheat samples with a HFN range of 62–318 s, including field and laboratory pre-germinated samples placed under wet conditions. Two different approaches were tested to apply calibrations: i) application of the calibration to each pixel, followed by calculation of the average of the resulting values for each object (kernel); ii) calculation of the average spectrum for each object, followed by application of the calibration to the mean spectrum. The calibration performance achieved for HFN (R2 = 0.6; RMSEC ~ 50 sRMSEP ~ 63 s) compares favourably with other studies using NIR spectroscopy. Linear spectral pre-treatments lead to similar results when applying the two methods, while non-linear treatments such as standard normal variate showed obvious differences between these approaches. A classification model based on linear discriminant analysis (LDA) was also applied to segregate wheat kernels into low (<250 s) and high (>250 s) HFN groups. LDA correctly classified 86.4% of the samples, with a classification accuracy of 97.9% when using an HFN threshold of 150 s. These results are promising in terms of wheat quality assessment using a rapid and non-destructive technique which is able to analyse wheat properties on a single-kernel basis, and to classify samples as acceptable or unacceptable for flour production.

Classification of Floral Origins of Honey by NIR and Chemometrics

2012 ◽  
Vol 605-607 ◽  
pp. 905-909 ◽  
Author(s):  
Xiu Ying Liang ◽  
Xiao Yu Li ◽  
Wen Jun Wu
Keyword(s):  
Spectral Data ◽  
Spectral Region ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Classification Rate ◽  
Standard Normal Variate ◽  
Mean Centering ◽  
Scattering Correction ◽  
Standard Normal

Near-infrared (NIR) spectroscopy combined with chemometrics methods has been investigated to discriminate type of honey. 147 NIR spectra of six floral origins of honey samples were collected within 4000~10000cm-1 spectral region. Spectral data were compressed using partial least squares (PLS). Back propagation neural networks (BPNN) models were constructed to distinguish the type of honey. Six spectral data pretreatments including first derivative, first derivatives followed by mean centering(MC), second derivatives, Savitzky-Golay smoothing, standard normal variate transformation (SNV) and multiplicative scattering correction (MSC) were compared to establish the optimal models for honey discrimination. Savitzky-Golay smoothing proved more effective than the other data pretreatments. BPNN models were developed within the full spectral region, 5303～6591cm-1 and 7012～10001cm-1, respectively. Results have shown that the highest(100%) classification rate was achieved within 5303～6591cm-1 wave range. Our results indicated that NIR spectroscopy with chemometrics techniques can be applied to classify rapidly honeys of different floral origin.

scholarly journals Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods

Foods ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 1778
Author(s):  
Fan Wang ◽  
Chunjiang Zhao ◽  
Guijun Yang
Keyword(s):  
Near Infrared ◽  
External Validation ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Spectral Ratio ◽  
Optical Path Length ◽  
Standard Normal Variate ◽  
Length Estimation ◽  
Standard Normal ◽  
Non Destructive

Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible−near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650–1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient (R2v) of 0.93 and root mean square error (RMSEv) of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears.

scholarly journals Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy

2019 ◽  
Vol 9 (8) ◽  
pp. 1654 ◽  
Author(s):  
Lei Lin ◽  
Yong He ◽  
Zhitao Xiao ◽  
Ke Zhao ◽  
Tao Dong ◽  
...  
Keyword(s):  
Moisture Content ◽  
Rapid Detection ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Rice Grain ◽  
Grain Moisture ◽  
And Storage

Rice grain moisture has a great impact on th production and storage storage quality of rice. The main objective of this study was to design and develop a rapid-detection sensor for rice grain moisture based on the Near-infrared spectroscopy (NIR) characteristic band, aiming to realize its accurate and on-line measurement. In this paper, the NIR spectral information of grain samples with different moisture content was obtained using a portable NIR spectrometer. Then, the partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were applied to model and analyze the spectral data to find the rice grain moisture NIR spectroscopy. As a result, the 1450 nm band was sensitive to the rice grain moisture and a rapid-detection sensor was developed with a 1450 nm light emitting diode (LED) light source, InGaAs photodiode, lens and filter, whose basic principle is to establish the relationship between the rice grain moisture and the measured voltage signal. To evaluate the sensor performance, rice grain samples with 13–30% moisture content were detected, the coefficient of determination R2 was 0.936, and the sum of squares for error (SSE) was 23.44. It is concluded that this study provides a spectroscopic measuring method, as well as developing an effective and accurate sensor for the rapid determination of rice grain moisture, which is of great significance for monitoring the quality of rice grain during its production, transportation and storage process.

scholarly journals Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy

Recycling ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Kirsti Cura ◽  
Niko Rintala ◽  
Taina Kamppuri ◽  
Eetta Saarimäki ◽  
Pirjo Heikkilä
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Automated Recognition ◽  
Textile Materials ◽  
Input Material ◽  
Adequate Quality ◽  
Automated Line

In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.

Sign in / Sign up

Export Citation Format

Share Document