scholarly journals Nondestructive Detection of Blackheart in Potato by Visible/Near Infrared Transmittance Spectroscopy

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Zhu Zhou ◽  
Songwei Zeng ◽  
Xiaoyu Li ◽  
Jian Zheng
Keyword(s):  
Near Infrared ◽  
Discrimination Performance ◽  
Principal Component ◽  
Spectroscopic Technique ◽  
Chemometric Methods ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Infrared Transmittance ◽  
Validation Set ◽  
Weight Correction

The possibility of using visible/near infrared (Vis/NIR) transmission spectroscopic technique in the 513–850 nm region coupled with partial least squares-linear discriminant analysis (PLS-LDA) and other chemometric methods to classify potatoes with blackheart was investigated. The discrimination performance of different morphological correction methods, including weight correction, height correction, and volume correction, was compared. The results showed that height corrected transmittance has the best performance, with both calibration and validation sets having a success rate of 97.11%. Out of 1800 wavelengths, only six wavelengths (711, 817, 741, 839, 678, and 698 nm) were selected as the optimum wavelengths for the discrimination of blackheart tubers based on principal component analysis (PCA). The data analysis showed that the overall classification rate by PLS-LDA method decreased from 97.11% to 96.82% in calibration set and from 97.11% to 96.53% in validation set, which was acceptable. The importance of these conclusions may be helpful to transfer Vis/NIR transmission technology from laboratory to industrial application in nondestructive, real-time, or portable measurement of potatoes quality.

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

Foods ◽  
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.

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.

DISCRIMINATIVE ANALYSIS OF FUNCTIONAL NEAR-INFRARED SPECTROSCOPY SIGNALS FOR DEVELOPMENT OF NEUROIMAGING BIOMARKERS OF ELDERLY DEPRESSION

2010 ◽  
Vol 03 (01) ◽  
pp. 69-74 ◽  
Author(s):  
YE ZHU ◽  
TIANZI JIANG ◽  
YUAN ZHOU ◽  
LISHA ZHAO
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Psychiatric Patients ◽  
Clinical Tool ◽  
Functional Near Infrared Spectroscopy ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Discriminative Model ◽  
Neuroimaging Biomarkers

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technology which is suitable for psychiatric patients. Several fNIRS studies have found abnormal brain activations during cognitive tasks in elderly depression. In this paper, we proposed a discriminative model of multivariate pattern classification based on fNIRS signals to distinguish elderly depressed patients from healthy controls. This model used the brain activation patterns during a verbal fluency task as features of classification. Then Pseudo-Fisher Linear Discriminant Analysis was performed on the feature space to generate discriminative model. Using leave-one-out (LOO) cross-validation, our results showed a correct classification rate of 88%. The discriminative model showed its ability to identify people with elderly depression and suggested that fNIRS may be an efficient clinical tool for diagnosis of depression. This study may provide the first step for the development of neuroimaging biomarkers based on fNIRS in psychiatric disorders.

CHEMOMETRICS ASSISTED SPECTROSCOPIC METHOD DEVELOPMENT AND VALIDATION FOR SIMULTANEOUS DETERMINATION OF SOFOSBUVIR AND DACLATASVIR DIHYDROCHLORIDE IN TABLET FORMULATION

INDIAN DRUGS ◽  
2020 ◽  
Vol 57 (03) ◽  
pp. 37-46
Author(s):  
Sapna M Rathod ◽  
Paresh U Patel
Keyword(s):  
Method Development ◽  
Spectroscopic Method ◽  
Principal Component ◽  
Tablet Formulation ◽  
Partial Least Square ◽  
Least Square ◽  
Simultaneous Estimation ◽  
Mixed Solution ◽  
Chemometric Methods ◽  
Validation Set

Four chemometric methods, namely Classical Least Square (CLS), Inverse Least Square (ILS), Partial Least Square (PLS) and Principal Component Regression (PCR), were developed for the simultaneous estimation of sofosbuvir and daclatasvir dihydrochloride in tablet formulation. Full factorial design was used to construct calibration set as well as validation set. Twenty five mixed solutions were prepared for calibration set and sixteen mixed solution of drugs were prepared for validation set. The absorbance of all prepared solutions was measured in the range of 230 nm to 335 nm at 16 wavelength points at an interval of 7 nm. Linearity was observed in the range of 10 – 90 µg/mL for sofosbuvir and 4 - 20 µg/mL for daclatasvir dihydrochloride. The developed chemometric methods were validated in terms of precision and accuracy as per ICH guidelines. The developed methods can be applied for the routine quantitative analysis of formulation.

scholarly journals Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4479 ◽  
Author(s):  
Xavier Cetó ◽  
Núria Serrano ◽  
Miriam Aragó ◽  
Alejandro Gámez ◽  
Miquel Esteban ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Reversed Phase ◽  
Classification Rate ◽  
Phenolic Profile ◽  
Linear Discriminant ◽  
Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

Effect of the chain length on near-infrared spectra of 1-alcohols from methanol to 1-decanol

NIR news ◽  
2019 ◽  
Vol 30 (3) ◽  
pp. 6-8
Author(s):  
Mirosław Antoni Czarnecki ◽  
Michał Kwaśniewicz
Keyword(s):  
Principal Component Analysis ◽  
Chain Length ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Principal Component ◽  
Pure Liquid ◽  
Chemometric Methods ◽  
Near Infrared Spectra ◽  
Similarities And Differences ◽  
Self Association

This work shows the effect of the chain length on near-infrared spectra of 1-alcohols and is based on a recent paper by Kwaśniewicz and Czarnecki ( Appl Spectrosc 2018, 72: 288). Near-infrared spectra of 1-alcohols from methanol to 1-decanol in the pure liquid phase were recorded from 5200 to 9000 cm−1. The similarities and differences between the spectra were analyzed by the classical and chemometric methods (principal component analysis). The obtained results reveal that the near-infrared spectra of methanol, ethanol, and 1-propanol are appreciably different from the spectra of higher 1-alcohols. As shown, the degree of self-association of 1-alcohols decreases with the increase in the chain length.

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

Foods ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 450 ◽  
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.

The Authentication of Basmati Rice Using near Infrared Spectroscopy: Some Further Analysis

1995 ◽  
Vol 3 (3) ◽  
pp. 111-117 ◽  
Author(s):  
W.J. Krzanowski
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Basmati Rice ◽  
Multivariate Techniques ◽  
Infrared Transmission ◽  
Linear Discriminant ◽  
Canonical Variates ◽  
Rice Samples ◽  
Single Category ◽  
Pooled Samples

The feasibility of using near infrared transmission spectroscopy to discriminate between Basmati and other long-grain rice samples has been demonstrated previously by Osborne et al. In their analysis they pooled samples from different countries of origin into the single category “other” and used the multivariate techniques of principal component analysis and linear discriminant functions to arrive at their conclusions. We reanalyse here their data but without such a major pooling of samples, retaining four groups in the discrimination. Using the multivariate techniques of partial least squares, orthogonal canonical variates and a recently proposed search for “extremeness”, we demonstrate complete support for the previous conclusions.

Identification Performance of Different Types of Handheld Near-Infrared (NIR) Spectrometers for the Recycling of Polymer Commodities

2018 ◽  
Vol 72 (9) ◽  
pp. 1362-1370 ◽  
Author(s):  
Hui Yan ◽  
Heinz W. Siesler
Keyword(s):  
Near Infrared ◽  
Raw Materials ◽  
Discrimination Performance ◽  
Principal Component ◽  
Identification Performance ◽  
Score Plot ◽  
Discrimination Capability ◽  
Standard Normal Variate ◽  
Correct Assignment ◽  
Standard Normal

For sustainable utilization of raw materials and environmental protection, the recycling of the most common polymers—polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS)—is an extremely important issue. In the present communication, the discrimination performance of the above polymer commodities based on their near-infrared (NIR) spectra measured with four real handheld (<200 g) spectrometers based on different monochromator principles were investigated. From a total of 43 polymer samples, the diffuse reflection spectra were measured with the handheld instruments. After the original spectra were pretreated by second derivative and standard normal variate (SNV), principal component analysis (PCA) was applied and unknown samples were tested by soft independent modeling of class analogies (SIMCA). The results show that the five polymer commodities cluster in the score plots of their first three principal components (PCs) and, furthermore, samples in calibration and test sets can be correctly identified by SICMA. Thus, it was concluded that on the basis of the NIR spectra measured with the handheld spectrometers the SIMCA analysis provides a suitable analytical tool for the correct assignment of the type of polymer. Because the mean distance between clusters in the score plot reflects the discrimination capability for each polymer pair the variation of this parameter for the spectra measured with the different handheld spectrometers was used to rank the identification performance of the five polymer commodities.

scholarly journals Evaluation of Visible-Near Infrared Reflectance Spectra of Avocado Leaves as a Non-destructive Sensing Tool for Detection of Laurel Wilt

Plant Disease ◽  
2012 ◽  
Vol 96 (11) ◽  
pp. 1683-1689 ◽  
Author(s):  
Sindhuja Sankaran ◽  
Reza Ehsani ◽  
Sharon A. Inch ◽  
Randy C. Ploetz
Keyword(s):  
Discriminant Analysis ◽  
Vegetation Index ◽  
Near Infrared ◽  
Principal Component ◽  
Persea Americana ◽  
Near Infrared Reflectance ◽  
Laurel Wilt ◽  
Linear Discriminant ◽  
Raffaelea Lauricola ◽  
Non Destructive

Laurel wilt, caused by the fungus Raffaelea lauricola, affects the growth, development, and productivity of avocado, Persea americana. This study evaluated the potential of visible-near infrared spectroscopy for non-destructive sensing of this disease. The symptoms of laurel wilt are visually similar to those caused by freeze damage (leaf necrosis). In this work, we performed classification studies with visible-near infrared spectra of asymptomatic and symptomatic leaves from infected plants, as well as leaves from freeze-damaged and healthy plants, both of which were non-infected. The principal component scores computed from principal component analysis were used as input features in four classifiers: linear discriminant analysis, quadratic discriminant analysis (QDA), Naïve-Bayes classifier, and bagged decision trees (BDT). Among the classifiers, QDA and BDT resulted in classification accuracies of higher than 94% when classifying asymptomatic leaves from infected plants. All of the classifiers were able to discriminate symptomatic-infected leaves from freeze-damaged leaves. However, the false negatives mainly resulted from asymptomatic-infected leaves being classified as healthy. Analyses of average vegetation indices of freeze-damaged, healthy (non-infected), asymptomatic-infected, and symptomatic-infected leaves indicated that the normalized difference vegetation index and the simple ratio index were statistically different.

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