scholarly journals Improvement of NIR models for quality parameters of leech and earthworm medicines using outlier multiple diagnoses

2017 ◽  
Vol 11 (01) ◽  
pp. 1750009 ◽  
Author(s):  
Chunyan Wu ◽  
Jiashan Chen ◽  
Mengru Li ◽  
Yongjiang Wu ◽  
Xuesong Liu
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Solid Content ◽  
Detection Methods ◽  
Soluble Solid Content ◽  
Relative Standard Error ◽  
Diffuse Reflection Spectroscopy ◽  
Nondestructive Technique ◽  
Relative Standard

Leeches and earthworms are the main ingredients of Shuxuetong injection compositions, which are natural biomedicines. Near infrared (NIR) diffuse reflection spectroscopy has been used for quality assurance of Chinese medicines. In the present work, NIR spectroscopy was proposed as a rapid and nondestructive technique to assess the moisture content (MC), soluble solid content (SSC) and hypoxanthine content (HXC) of leeches and earthworms. This study goal was to improve NIR models for accurate quality control of leech and earthworm using outlier multiple diagnoses (OMD). OMD was composed of four outlier detection methods: spectrum outlier diagnostic (MD), leverage diagnostic (LD), principal component scores diagnostic (PCSD) and factor loading diagnostic (FLD). Conventional outlier diagnoses (MD, LD) and OMD were compared, and the best NIR models were those based on OMD. The correlation coefficients ([Formula: see text]) for leech were 0.9779, 0.9616 and 0.9406 for MC, SSC and HXC, respectively. The values of relative standard error of prediction (RSEP) for leech were 2.3%, 5.1% and 9.0% for MC, SSC and HXC, respectively. The values of [Formula: see text] for earthworm were 0.9478, 0.9991 and 0.9605 for MC, SSC and HXC, respectively. The values of RSEP for earthworm were 8.8%, 2.4% and 12% for MC, SSC and HXC, respectively. The performance of the NIR models was certainly improved by OMD.

scholarly journals Near-Infrared Spectroscopy and Chemometrics for the Routine Detection of Bilberry Extract Adulteration and Quantitative Determination of the Anthocyanins

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Claudio Gardana ◽  
Antonio Scialpi ◽  
Christian Fachechi ◽  
Paolo Simonetti
Keyword(s):  
Near Infrared ◽  
Spectroscopic Method ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Good Prediction ◽  
Anthocyanin Content ◽  
Least Squares Regression ◽  
Nondestructive Technique ◽  
Total Anthocyanin ◽  
Bilberry Extract

Consumers must be assured that bought food supplements contain both bilberry extract and the anthocyanin amounts that match the declared levels. Therefore, a Fourier transform near-infrared (FT-NIR) spectroscopic method was validated based on principal component scores for the prediction of bilberry extract adulteration and partial least squares regression model for total anthocyanin evaluation. Anthocyanins have been quantified individually in 71 commercial bilberry extracts by HPLC-DAD, and 6 of them were counterfeit. The anthocyanin content in bilberry extracts was in the range 18–34%. Authentic bilberry extracts (n=65) were divided into two parts: one for calibration (n=38) and the other for the validation set (n=27). Spectra were recorded in the range of 4000–12500 cm−1, and a good prediction model was obtained in the range of 9400–6096 and 5456–4248 cm−1withr2of 99.5% and a root-mean-square error of 0.3%. The adulterated extracts subjected to NIR analysis were recognized as noncompliant, thus confirming the results obtained by chromatography. The FT-NIR spectroscopy is an economic, powerful, and fast methodology for the detection of adulteration and quantification of the total anthocyanin in bilberry extracts; above all, it is a rapid, low cost, and nondestructive technique for routine analysis.

scholarly journals Near infrared spectroscopy determination of chemical and sensory properties in tomato

2021 ◽  
pp. 096703352110187
Author(s):  
Dong Sun ◽  
Jordi Cruz ◽  
Manel Alcalà ◽  
Roser Romero del Castillo ◽  
Silvia Sans ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Sensory Properties ◽  
Soluble Solids ◽  
Taste Intensity ◽  
Relative Standard Error ◽  
Tomato Puree ◽  
Relative Standard ◽  
Solids Content ◽  
Chemical Traits

Fast and massive characterization of quality attributes in tomatoes is a necessary step toward its improvement; for sensory attributes this process is time-consuming and very expensive, which causes its absence in routine phenotpying. We aimed to assess the feasibility of near infrared (NIR) spectroscopy as a fast and economical tool to predict both the chemical and sensory properties of tomatoes. We built partial least squares models from spectra recorded from tomato puree and juice in 53 genetically diverse varieties grown in two environments. Samples were divided in calibration (210 samples for chemical traits, 45 samples for sensory traits) and validation sets (60 and 10, respectively) using the Kennard Stone algorithm. Models from puree spectra gave validation r2 values higher than 0.97 for fructose, glucose, soluble solids content, and dry matter (relative standard error of prediction, RSEP% ranged 3.5–5.8), while r2 values for sensory properties were lower (ranging 0.702–0.917 for taste-related traits (RSEP%: 9.1–20.0), and 0.009–0.849 for texture related traits (RSEP%: 3.6–72.1)). For sensory traits such as explosiveness, juiciness, sweetness, acidity, taste intensity, aroma intensity, and mealiness, NIR spectroscopy is potentially useful for scanning large collections of samples to identify likely candidates to select for tomato quality.

scholarly journals Algorithms for detecting cherry pits on the basis of transmittance mode hyperspectral data

2017 ◽  
Vol 31 (4) ◽  
pp. 539-549 ◽  
Author(s):  
Anna Siedliska ◽  
Monika Zubik ◽  
Piotr Baranowski ◽  
Wojciech Mazurek
Keyword(s):  
Near Infrared ◽  
Imaging Technique ◽  
Principal Component ◽  
Solid Content ◽  
Hyperspectral Data ◽  
Infrared Range ◽  
Soluble Solid Content ◽  
Soluble Solid ◽  
Pre Treatment ◽  
Validation Set

Abstract The suitability of the hyperspectral transmittance imaging technique was assessed in terms of detecting the internal intrusions (pits and their fragments) in cherries. Herein, hyperspectral transmission images were acquired in the visible and near-infrared range (450-1000 nm) from pitted and intact cherries of three popular cultivars: ‘Łutówka’, ‘Pandy 103’, and ‘Groniasta’, differing by soluble solid content. The hyperspectral transmittance data of fresh cherries were used to determine the influence of differing soluble solid content in fruit tissues on pit detection effectiveness. Models for predicting the soluble solid content of cherries were also developed. The principal component analysis and the second derivative pre-treatment of the hyperspectral data were used to construct the supervised classification models. In this study, five classifiers were tested for pit detection. From all the classifiers studied, the best prediction accuracies for the whole pit or pit fragment detection were obtained via the backpropagation neural networks model (87.6% of correctly classified instances for the training/test set and 81.4% for the validation set). The accuracy of distinguishing between drilled and intact cherries was close to 96%. These results showed that the hyperspectral transmittance imaging technique is feasible and useful for the non-destructive detection of pits in cherries.

scholarly journals Adulterated stingless bee honey identification using VIS-NIR spectroscopy technique

Food Research ◽  
2021 ◽  
Vol 5 (S1) ◽  
pp. 85-93
Author(s):  
M.F.J. Azmi ◽  
D. Jamaludin ◽  
S. Abd. Aziz ◽  
Y.A. Yusof ◽  
A.M. Mustafah
Keyword(s):  
Nir Spectroscopy ◽  
Principal Component ◽  
Solid Content ◽  
Stingless Bee ◽  
Soluble Solid Content ◽  
Spectroscopy Technique ◽  
Accuracy Rate ◽  
Linear Discriminant ◽  
Different Types ◽  
Soluble Solid

The objective of this study was to study the ability of the VIS-NIR spectroscopy to classify the pure and adulterated stingless bee honey across the wavelength range of 450– 969 nm using an optical spectrometer. The physicochemical properties such as soluble solid content (SSC) and moisture content (refractive index, RI) of pure and adulterated honey has also been investigated using a refractometer. The result showed that pure stingless bee honey has the highest transmittance rate, SSC and RI value compared to adulterated honey. There are significant differences (P < 0.0001) in the transmittance rate, SSC and RI of stingless bee honey over five different types of treatments. The results also showed that VIS-NIR data were good in classifying the samples into different treatments with 99.33% accuracy rate. About thirty-four wavelengths were found to be the most significant to discriminate the different treatments by principal component analysis (PCA) and linear discriminant analysis (LDA) techniques.

scholarly journals Two Different Portables Visible-Near Infrared and Shortwave Infrared Region for On-Tree Measurement of Soluble Solid Content of Marian Plum Fruit

2020 ◽  
Vol 24 (5) ◽  
pp. 227-236
Author(s):  
Jetsada Posom ◽  
Navavit Soonnamtiang ◽  
Patcharapong Kotethum ◽  
Pakhpoom Konjun ◽  
Panmanas Sirisomboon ◽  
...  
Keyword(s):  
Near Infrared ◽  
Solid Content ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Soluble Solid Content ◽  
Relative Standard ◽  
Soluble Solid ◽  
Shortwave Infrared ◽  
Mean Square Errors

The goal of this study was to predict the soluble solid content (SSC) of on-tree Marian plum fruit using two different wavelength range and algorithm. One of these was the commercial dispersion NIR spectrometer (MicroNIR 1700), providing shortwave infrared (SWIR), while the other was a making diode array spectrometer giving visible-near infrared (Vis-NIR). To search optimal model, the analytical ability of the two wavelength ranges spectrometers coupled with two algorithms: i.e. partial least squares regression (PLSR) and support vector machine regression (SVR), were investigated. Different spectral pre-processing methods were tested. The model providing the lowest root mean square errors of prediction (RMSEP) was selected. Overall, the proposed outcome was that the performance of SWIR was more accurate than Vis-NIR spectrometer, and that both SWIR and Vis-NIR coupled with PLSR algorithm had a higher accuracy than SVR algorithm. The best model for on-tree evaluation SSC was the SWIR constructed using the PLSR algorithm with the spectral pre-processing of the 2nd derivative, providing a coefficient of determination of calibration set (R2) of 0.81, a coefficient of determination of validation set (r2) of 0.76, RMSEP of 0.69 °Brix, and a relative standard error of prediction (RSEP) of 4.43%. The outcome showed that a portable SWIR spectrometer developed with PLSR could be used for monitoring the SSC of individual Marian plum fruit on-tree for quality assurance.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

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

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.

scholarly journals Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy

2018 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
João S. Panero ◽  
Henrique E. B. da Silva ◽  
Pedro S. Panero ◽  
Oscar J. Smiderle ◽  
Francisco S. Panero ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Relevant Information ◽  
Whole Grains ◽  
Signal Correction ◽  
Different Types ◽  
Linear Fit

Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm-1), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm-1 to 4570.9 cm-1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

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