scholarly journals Discriminant analysis of pyrrolizidine alkaloid contamination in bee pollen based on near-infrared data from lab-stationary and portable spectrometers

2020 ◽  
Vol 246 (12) ◽  
pp. 2471-2483
Author(s):  
Luciana De Jesus Inacio ◽  
Ilaria Lanza ◽  
Roberta Merlanti ◽  
Barbara Contiero ◽  
Lorena Lucatello ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Pyrrolizidine Alkaloid ◽  
Nir Spectroscopy ◽  
Misclassification Rate ◽  
Bee Pollen ◽  
Chemical Structures ◽  
Main Challenge ◽  
Portable Spectrometers ◽  
Leave One Out

AbstractBee pollen may be contaminated with pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs), which are mainly detected by liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS), even though the use of fast near-infrared (NIR) spectroscopy is an ongoing alternative. Therefore, the main challenge of this study was to assess the feasibility of both a lab-stationary (Foss) and a portable (Polispec) NIR spectrometer in 60 dehydrated bee pollen samples. After an ANOVA-feature selection of the most informative NIR spectral data, canonical discriminant analysis (CDA) was performed to distinguish three quantitative PA/PANO classes (µg/kg): < LOQ (0.4), low; 0.4–400, moderate; > 400, high. According to the LC–MS/MS analysis, 77% of the samples were contaminated with PAs/PANOs and the sum content of the 17 target analytes was higher than 400 µg/kg in 28% of the samples. CDA was carried out on a pool of 18 (Foss) and 22 (Polispec) selected spectral variables and allowed accurate classification of samples from the low class as confirmed by the high values of Matthews correlation coefficient (≥ 0.91) for both NIR spectrometers. Leave-one-out cross-validation highlighted precise recognition of samples characterised by a high PA/PANO content with a low misclassification rate (0.02) as false negatives. The most informative wavelengths were within the < 1000, 1000–1660 and > 2400 nm regions for Foss and > 1500 nm for Polispec that could be associated with cyclic amines, and epoxide chemical structures of PAs/PANOs. In sum, both lab-stationary and portable NIR systems are reliable and fast techniques for detecting PA/PANO contamination in bee pollen.

scholarly journals Assessment of chicken breast shelf life based on bench-top and portable NIR spectroscopy tools coupled with chemometrics

2020 ◽  
Author(s):  
Ilaria Lanza ◽  
Daniele Conficoni ◽  
Stefania Balzan ◽  
Marco Cullere ◽  
Luca Fasolato ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Shelf Life ◽  
Meat Quality ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Life Assessment ◽  
Chicken Breast ◽  
Portable Devices ◽  
Physicochemical Processes ◽  
Insight Into

Abstract Near-infrared (NIR) spectroscopy is a rapid technique able to assess meat quality even if its capability to determine the shelf life of chicken fresh cuts is still debated, especially for portable devices. The aim of the study was to compare bench-top and portable NIR instruments in discriminating between four chicken breast refrigeration times (RT), coupled with multivariate classifier models. Ninety-six samples were analysed by both NIR tools at 2, 6, 10 and 14 days post-mortem. NIR data were subsequently submitted to partial least squares discriminant analysis (PLS-DA) and canonical discriminant analysis (CDA). The latter was preceded by double feature selection based on Boruta and Stepwise procedures. PLS-DA sorted moderate separation of RT theses, while shelf life assessment was more accurate on application of Stepwise-CDA. Bench-top tool had better performance than portable one, probably because it captured more informative spectral data as shown by the variable importance in projection (VIP) and restricted pool of Stepwise-CDA predictive scores (SPS). NIR tools coupled with a multivariate model provide deep insight into the physicochemical processes occurring during storage. Spectroscopy showed reliable effectiveness to recognise a 7-day shelf life threshold of breasts, suitable for routine at-line application for screening of meat quality.

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.

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 Discrimination of Salvia miltiorrhiza Bunge from Different Geographical Locations Employing High-Performance Liquid Chromatography, Near-Infrared Fingerprinting Combined with Chemometrics

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jiao Wang ◽  
Yichun Sun ◽  
Zhan Li ◽  
Wei Li ◽  
Yuanyuan Pang ◽  
...  
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Discriminant Analysis ◽  
High Performance ◽  
Near Infrared ◽  
Salvia Miltiorrhiza ◽  
Nir Spectroscopy ◽  
Gradient Elution ◽  
Array Detector ◽  
Geographical Locations

To evaluate the quality of Salvia miltiorrhiza Bunge, high-performance liquid chromatography-diode array detector (HPLC/UV-PAD), near infrared (NIR) spectroscopy, and chemometrics were used to discriminate nine components of samples from four different geographical locations. HPLC was performed with a C18 (5 μm, 4.6 mm × 250 mm) column and 0.1% formic acid aqueous solution-acetonitrile with a gradient elution system. Orthogonal partial least squares discriminant analysis was used to identify the amounts of salvianolic acid B. NIR was used to distinguish rapidly S. miltiorrhiza Bunge samples from different geographical locations. In this assay, discriminant analysis was performed, and the accuracy was found to be 100%. The combination of these two methods can be used to quickly and accurately identify S. miltiorrhiza Bunge from different geographical locations.

Authentication of organic sugars by NIR spectroscopy and partial least squares with discriminant analysis

2020 ◽  
Vol 12 (5) ◽  
pp. 701-705 ◽  
Author(s):  
Vitória Maria Almeida Teodoro de Oliveira ◽  
Michel Rocha Baqueta ◽  
Paulo Henrique Março ◽  
Patrícia Valderrama
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Nir Spectroscopy

The present study evaluated the potential of near-infrared (NIR) spectroscopy coupled with partial least squares with discriminant analysis (PLS-DA) for the authentication of organic sugars.

scholarly journals Discrimination of E uterpe oleracea  Mart. (Açaí) and E uterpe edulis  Mart. (Juçara) Intact Fruit Using Near-Infrared (NIR) Spectroscopy and Linear Discriminant Analysis

2015 ◽  
Vol 39 (6) ◽  
pp. 2856-2865 ◽  
Author(s):  
Yara Gurgel Dall' Acqua ◽  
Luis Carlos Cunha Júnior ◽  
Viviani Nardini ◽  
Valquira Garcia Lopes ◽  
José Dalton da Cruz Pessoa ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Linear Discriminant

scholarly journals Rapid Recognition of Geoherbalism and Authenticity of a Chinese Herb by Data Fusion of Near-Infrared Spectroscopy (NIR) and Mid-Infrared (MIR) Spectroscopy Combined with Chemometrics

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Haiyan Fu ◽  
Qiong Shi ◽  
Liuna Wei ◽  
Lu Xu ◽  
Xiaoming Guo ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Data Fusion ◽  
Near Infrared ◽  
Chinese Herb ◽  
Nir Spectroscopy ◽  
Chinese Herbs ◽  
Angelica Dahurica ◽  
Mid Infrared ◽  
Linear Discriminant ◽  
Mir Spectroscopy

Fourier transform near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectroscopy play important roles in all fingerprint techniques because of their unique characteristics such as reliability, versatility, precision, and ease of measurement. In this paper, a supervised pattern recognition method based on the PLSDA algorithm by NIR and the NIR-MIR fusion spectra has been established to identify geoherbalism of Angelica dahurica from different regions and authenticity of Corydalis yanhusuo W. T. Wang. Comparing principle component analysis (PCA) cannot successfully identify geographical origins of Angelica dahurica. Linear discriminant analysis (LDA) also hardly distinguishes those origins. Furthermore, the PLSDA model based on the data fusion of NIR and IR was more accurate and efficient. But, the identification of authenticity of Corydalis yanhusuo W. T. Wang was still inaccurate in the PLSDA model. Consequently, data fusion of NIR-MIR original spectra combined with moving window partial least-squares discriminant analysis was firstly used and showed perfect properties on authenticity and adulteration discrimination of Corydalis yanhusuo W. T. Wang. It indicated that data fusion of NIR-MIR spectra combined with MWPLSDA could be considered as the promising tool for rapid discrimination of the geoherbalism and authenticity of more Chinese herbs in the future.

Comparative non-destructive classification of partial waxy wheats using near-infrared and Raman spectroscopy

2019 ◽  
Vol 70 (5) ◽  
pp. 437 ◽  
Author(s):  
Dongli Liu ◽  
Yixuan Wu ◽  
Zongmei Gao ◽  
Yong-Huan Yun
Keyword(s):  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Triticum Aestivum L ◽  
Waxy Wheat ◽  
Waxy Proteins ◽  
Sample Set

Waxy proteins play a key role in amylose synthesis in wheat. Eight lines of common wheat (Triticum aestivum L.) carrying mutations in the three homoeologous waxy loci, Wx-A1, Wx-B1 and Wx-D1, have been classified by near-infrared (NIR) and Raman spectroscopy combined with chemometrics. Sample spectra from wheat seeds were collected by using a NIR spectrometer in the wave rage 1600–2400 nm, and then Raman spectrometer in the wave range 700–2000 cm–1. All samples were split randomly into a calibration sample set containing 284 seeds (~35 seeds per line) and a validation sample set containing the remaining 92 seeds. Classification of these samples was undertaken by discriminant analysis combined with principal component analysis (PCA) based on the raw spectra processed by appropriate pre-treatment methods. The classification results by discriminant analysis indicated that the percentage of correctly identified samples by NIR spectroscopy was 84.2% for the calibration set and 84.8% for the validation set, and by Raman spectroscopy 94.4% and 94.6%, respectively. The results demonstrated that Raman spectroscopy combined with chemometrics as a rapid method is superior to NIR spectroscopy in classifying eight partial waxy wheat lines with different waxy proteins.

Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef

2021 ◽  
pp. 096703352110495
Author(s):  
Cassius EO Coombs ◽  
Robert R Liddle ◽  
Luciano A González
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Consumer Confidence ◽  
Spectroscopic Methods ◽  
Marbling Score ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Beef Steak

The present study analysed the ability for portable near infrared reflectance (NIR) and Raman spectroscopy sensors to differentiate between grass-fed and grain-fed beef. Scans were made on lean and fat surfaces of 108 beef steak samples labelled as grass-fed ( n = 54) and grain-fed ( n = 54), with partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) used to develop discrimination models which were tested on independent datasets. Furthermore, PLS-DA was used to predict visual marbling score and days on feed (DOF). The NIR spectra accurately discriminated between grass- and grain-fed beef on both fat (91.7%, n = 92) and lean (88.5%, n = 96), as did Raman (fat 95.2%, n = 82; lean 69.6%, n = 68). Fat scanning using NIR spectroscopy moderately predicted DOF (r2val = 0.53), though Raman and NIR spectroscopy lean prediction models for DOF and marbling were less precise (r2val < 0.50). It can be concluded that portable NIR and Raman spectrometers can be used successfully to differentiate grass-fed from grain-fed beef and therefore aid retail and consumer confidence.

scholarly journals Near-infrared spectroscopy is feasible to discriminate hazelnut cultivars

2013 ◽  
Vol 44 (2s) ◽  
Author(s):  
Elisabetta Stella ◽  
Roberto Moscetti ◽  
Letizia Carletti ◽  
Giuseppina Menghini ◽  
Francesco Fabrizi ◽  
...  
Keyword(s):  
Amino Acids ◽  
Discriminant Analysis ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Functional Group ◽  
Nir Spectroscopy ◽  
Linear Discriminant ◽  
Minimum Error ◽  
Small Set ◽  
Nm Range

The study demonstrated the feasibility of the near infrared (NIR) spectroscopy use for hazelnut-cultivar sorting. Hazelnut spectra were acquired from 600 fruit for each cultivar sample, two diffuse reflectance spectra were acquired from opposite sides of the same hazelnut. Spectral data were transformed into absorbance before the computations. A different variety of spectral pretreatments were applied to extract characteristics for the classification. An iterative Linear Discriminant Analysis (LDA) algorithm was used to select a relatively small set of variables to correctly classify samples. The optimal group of features selected for each test was analyzed using Partial Least Squares Discriminant Analysis (PLS-DA). The spectral region most frequently chosen was the 1980-2060 nm range, which corresponds to best differentiation performance for a total minimum error rate lower than 1.00%. This wavelength range is generally associated with stretching and bending of the N-H functional group of amino acids and proteins. The feasibility of using NIR Spectroscopy to distinguish different hazelnut cultivars was demonstrated.

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