scholarly journals Mid-Infrared Spectroscopy as a Rapid Tool to Qualitatively Predict the Effects of Species, Regions and Roasting on the Nutritional Composition of Australian Acacia Seed Species

Molecules ◽  
2021 ◽  
Vol 26 (7) ◽  
pp. 1879
Author(s):  
Oladipupo Q. Adiamo ◽  
Yasmina Sultanbawa ◽  
Daniel Cozzolino
Keyword(s):  
Principal Component ◽  
Nutritional Composition ◽  
Human Consumption ◽  
Mid Infrared ◽  
Mir Spectroscopy ◽  
As Species ◽  
Before And After ◽  
Mir Spectra ◽  
Main Components ◽  
Rapid Tool

In recent times, the popularity of adding value to under-utilized legumes have increased to enhance their use for human consumption. Acacia seed (AS) is an underutilized legume with over 40 edible species found in Australia. The study aimed to qualitatively characterize the chemical composition of 14 common edible AS species from 27 regions in Australia using mid-infrared (MIR) spectroscopy as a rapid tool. Raw and roasted (180 °C, 5, 7, and 9 min) AS flour were analysed using MIR spectroscopy. The wavenumbers (1045 cm−1, 1641 cm−1, and 2852–2926 cm−1) in the MIR spectra show the main components in the AS samples. Principal component analysis (PCA) of the MIR data displayed the clustering of samples according to species and roasting treatment. However, regional differences within the same AS species have less of an effect on the components, as shown in the PCA plot. Statistical analysis of absorbance at specific wavenumbers showed that roasting significantly (p < 0.05) reduced the compositions of some of the AS species. The results provided a foundation for hypothesizing the compositional similarity and/or differences among AS species before and after roasting.

scholarly journals Romanian Wines Quality and Authenticity Using FT-MIR Spectroscopy Coupled with Multivariate Data Analysis

2014 ◽  
Vol 42 (2) ◽  
pp. 556-564 ◽  
Author(s):  
Roxana BANC ◽  
Felicia LOGHIN ◽  
Doina MIERE ◽  
Florinela FETEA ◽  
Carmen SOCACIU
Keyword(s):  
Amino Acids ◽  
Data Analysis ◽  
Organic Acids ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Mir Spectroscopy ◽  
Mir Spectra ◽  
Main Components

Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) combined with multivariate data analysis have been applied for the discrimination of 15 different Romanian wines (white, rosé and red wines), obtained from different origin-denominated cultivars. Principal component analysis and hierarchical cluster analysis was performed using different regions of FT-MIR spectra for all wines. The general fingerprint of wines was splitted in four characteristic regions, corresponding to phenolic derivatives, carbohydrates, amino acids and organic acids, which confer the wines quality and authenticity. By qualitative and quantitative evaluation of each component category, it was possible to discriminate each wine category, from red, to rosé and white colours, to dry, half-dry and half-sweet flavours. The multivariate data analysis based on absorption peaks from FT-MIR spectra demonstrated a very good, significant clustering of samples, based on the four main components: phenolics, carbohydrates, amino acids and organic acids. Therefore, the ATR-FT-MIR analysis proved to be a very fast, cheap and efficient tool to evaluate the quality and authenticity of wines, and to discriminate each wine category, based on their colour and sweetness, as consequence of their biological (cultivar) specificity.

scholarly journals Comparison and Identification for Rhizomes and Leaves of Paris yunnanensis Based on Fourier Transform Mid-Infrared Spectroscopy Combined with Chemometrics

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3343 ◽  
Author(s):  
Yi-Fei Pei ◽  
Qing-Zhi Zhang ◽  
Zhi-Tian Zuo ◽  
Yuan-Zhong Wang
Keyword(s):  
Fourier Transform ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Chemical Information ◽  
Accurate Identification ◽  
Mid Infrared ◽  
Model Classification ◽  
Paris Polyphylla ◽  
Mir Spectra ◽  
The Individual

Paris polyphylla, as a traditional herb with long history, has been widely used to treat diseases in multiple nationalities of China. Nevertheless, the quality of P. yunnanensis fluctuates among from different geographical origins, so that a fast and accurate classification method was necessary for establishment. In our study, the geographical origin identification of 462 P. yunnanensis rhizome and leaf samples from Kunming, Yuxi, Chuxiong, Dali, Lijiang, and Honghe were analyzed by Fourier transform mid infrared (FT-MIR) spectra, combined with partial least squares discriminant analysis (PLS-DA), random forest (RF), and hierarchical cluster analysis (HCA) methods. The obvious cluster tendency of rhizomes and leaves FT-MIR spectra was displayed by principal component analysis (PCA). The distribution of the variable importance for the projection (VIP) was more uniform than the important variables obtained by RF, while PLS-DA models obtained higher classification abilities. Hence, a PLS-DA model was more suitably used to classify the different geographical origins of P. yunnanensis than the RF model. Additionally, the clustering results of different geographical origins obtained by HCA dendrograms also proved the chemical information difference between rhizomes and leaves. The identification performances of PLS-DA and the RF models of leaves FT-MIR matrixes were better than those of rhizomes datasets. In addition, the model classification abilities of combination datasets were higher than the individual matrixes of rhizomes and leaves spectra. Our study provides a reference to the rational utilization of resources, as well as a fast and accurate identification research for P. yunnanensis samples.

Rapid Method for the Discrimination of Romanian Wines Based on Mid-Infrared Spectroscopy and Chemometrics

2018 ◽  
Vol 69 (2) ◽  
pp. 469-473
Author(s):  
Carmen Mihaela Topala ◽  
Lavinia Diana Tataru
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Multivariate Data Analysis ◽  
Geographical Origin ◽  
Principal Component ◽  
Component Analysis ◽  
Red Wines ◽  
Chemical Parameters ◽  
Mid Infrared ◽  
Mir Spectra

ATR-FTIR Spectroscopy combined with multivariate data analysis have been applied for the discrimination of 10 different Romanian wines (white and red wines), produced in 2 wineries from Romania: Reca� and Stefanesti-Arge�s from different cultivars. Principal Component Analysis were performed using different regions of FT-MIR spectra for all wines. Principal Component Analysis of their chemical parameters indicated that the wines can be discriminated based on their different phenolic, glucides, acidity content and geographical origin.

scholarly journals Genetic and Non-Genetic Variation of Milk Total Antioxidant Activity Predicted from Mid-Infrared Spectra in Holstein Cows

Animals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2372
Author(s):  
Giovanni Niero ◽  
Angela Costa ◽  
Marco Franzoi ◽  
Giulio Visentin ◽  
Martino Cassandro ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Selection Index ◽  
Water Soluble ◽  
Holstein Cows ◽  
Total Antioxidant Activity ◽  
Mid Infrared ◽  
Positive Effects ◽  
Mir Spectroscopy ◽  
Total Antioxidant ◽  
Mir Spectra

Food antioxidants enhance products shelf life and stability during technological treatments through the maintenance of their physical and chemical properties. Moreover, they are endowed with several positive effects on human health, including cell membranes preservation, enzyme functionality, and DNA integrity. Milk has been described in relation to a wide array of fat soluble and water-soluble antioxidant compounds, in particular vitamin A, C, and E, lactoferrin and peptides derived from casein and whey proteins. The total antioxidant activity (TAA) of milk is a novel and scarcely explored trait, defined as the sum of antioxidant contributions of the aforementioned compounds. On this background, the aims of the present study were to investigate the variability of milk TAA on a large scale exploiting predictions obtained through mid-infrared (MIR) spectroscopy and to estimate genetic parameters of this trait in Holstein cows. Individual milk samples were collected between January 2011 and December 2018 during the routine milk recording procedure. Samples were analysed for gross composition through MIR spectroscopy and MIR spectra were stored. Milk TAA was then predicted (pTAA) from the stored milk MIR spectra (111,653 test-day records of 9519 cows in 344 herds) using the previously developed prediction model; considering the prediction accuracy, pTAA might be considered a proxy of the TAA determined through the reference method. Overall, pTAA averaged 7.16 mmoL/L of Trolox equivalents, showed a nadir around 40 days after calving and increased thereafter, following a linear trend up to the end of lactation. The lowest pTAA was observed in milk sampled from June to September. Milk pTAA was heritable (0.401 ± 0.015) and genetically associated to fat yield (0.366 ± 0.049), crude protein (CP) yield (0.238 ± 0.052), fat percentage (0.616 ± 0.022) and CP percentage (0.754 ± 0.015). The official selection index of Italian Holstein put the 49% of the emphasis on fat and protein yield and percentage; therefore, it derives that an indirect favourable selection for milk pTAA should be already in progress in Italian Holstein population.

scholarly journals Can FT-Mid-Infrared Spectroscopy of Milk Samples Discriminate Different Dietary Regimens of Sheep Grazing With Restricted Access Time?

2021 ◽  
Vol 8 ◽  
Author(s):  
Giovanni Molle ◽  
Andrea Cabiddu ◽  
Mauro Decandia ◽  
Maria Sitzia ◽  
Ignazio Ibba ◽  
...  
Keyword(s):  
Principal Component ◽  
Italian Ryegrass ◽  
Access Time ◽  
Sheep Grazing ◽  
Forage Crops ◽  
Mid Infrared ◽  
Linear Discriminant ◽  
Best Fitting ◽  
Grazing Period ◽  
Mir Spectra

Milk obtained from sheep grazing natural pastures and some forage crops may be worth a plus value as compared to milk obtained from stall-fed sheep, due to their apparently higher content of beneficial fatty acids (FAs). Fourier transformed mid-infrared (FT-MIR) analysis of FA can help distinguish milk from different areas and diverse feeding systems. The objective was to discriminate milk from sheep and milk from dairy sheep rotationally grazing Italian ryegrass or berseem clover for 2, 4, or 6 h/day. To test this hypothesis, a data-mining study was undertaken using a database of 1,230 individual milk spectra. Data were elaborated by principal component analysis (PCA) and analyzed by linear discriminant analysis (LDA) with or without the use of genetic algorithm (GA) as a variable selection tool with the primary aim to discriminate grazed forages (grass vs. legume), access time (2, 4, or 6 h/day), grazing day (first vs. last grazing day during the 7-day grazing period), and the milking time (morning vs. afternoon milking). The best-fitting discriminant models of FT-MIR spectra were able to correctly predict 100% of the samples differing for the pasture forage, 91.9% of the samples differing for grazing day, and 97.1% of the samples regarding their milking time. The access time (AT) to pasture was correctly predicted by the model in 60.3% of the samples, and the classification ability was improved to 77.0% when considering only the 2 and 6 h/day classes.

scholarly journals Mastitis Detection from Milk Mid-Infrared (MIR) Spectroscopy in Dairy Cows

2019 ◽  
Vol 67 (5) ◽  
pp. 1221-1226 ◽  
Author(s):  
Lisa Rienesl ◽  
Negar Khayatzadeh ◽  
Astrid Köck ◽  
Laura Dale ◽  
Andreas Werner ◽  
...  
Keyword(s):  
Prediction Model ◽  
Time Window ◽  
Time Windows ◽  
Genetic Correlations ◽  
Recording System ◽  
Data Set ◽  
Mid Infrared ◽  
Calibration And Validation ◽  
Mir Spectroscopy ◽  
Mir Spectra

Mid-infrared (MIR) spectroscopy is the method of choice for the standard milk recording system, to determine milk components including fat, protein, lactose and urea. Since milk composition is related to health and metabolic status of a cow, MIR spectra could be potentially used for disease detection. In dairy production, mastitis is one of the most prevalent diseases. The aim of this study was to develop a calibration equation to predict mastitis events from routinely recorded MIR spectra data. A further aim was to evaluate the use of test day somatic cell score (SCS) as covariate on the accuracy of the prediction model. The data for this study is from the Austrian milk recording system and its health monitoring system (GMON). Test day data including MIR spectra data was merged with diagnosis data of Fleckvieh, Brown Swiss and Holstein Friesian cows. As prediction variables, MIR absorbance data after first derivatives and selection of wavenumbers, corrected for days in milk, were used. The data set contained roughly 600,000 records and was split into calibration and validation sets by farm. Calibration sets were made to be balanced (as many healthy as mastitis cases), while the validation set was kept large and realistic. Prediction was done with Partial Least Squares Discriminant Analysis, key indicators of model fit were sensitivity and specificity. Results were extracted for association between spectra and diagnosis with different time windows (days between diagnosis and test days) in validation. The comparison of different sets of predictor variables (MIR, SCS, MIR + SCS) showed an advantage in prediction for MIR + SCS. For this prediction model, specificity was 0.79 and sensitivity was 0.68 in time window -7 to +7 days (calibration and validation). Corresponding values for MIR were 0.71 and 0.61, for SCS they were 0.81 and 0.62. In general, prediction of mastitis performed better with a shorter distance between test day and mastitis event, yet even for time windows of -21 to +21 days, prediction accuracies were still reasonable, with sensitivities ranging from 0.50 to 0.57 and specificities remaining unchanged (0.71 to 0.85). Additional research to further improve prediction equation, and studies on genetic correlations among clinical mastitis, SCS and MIR predicted mastitis are planned.

Ultra-violet, visible, near-infrared, and mid-infrared diffuse reflectance spectroscopic techniques to predict several soil properties

Soil Research ◽  
2005 ◽  
Vol 43 (6) ◽  
pp. 713 ◽  
Author(s):  
Adam Pirie ◽  
Balwant Singh ◽  
Kamrunnahar Islam
Keyword(s):  
Organic Carbon ◽  
Soil Properties ◽  
Near Infrared ◽  
Principal Component ◽  
Electromagnetic Spectrum ◽  
Reference Laboratory ◽  
Spectroscopic Techniques ◽  
Mid Infrared ◽  
Exchangeable Ca ◽  
Mir Spectra

Reflectance spectroscopy techniques in the ultraviolet, visible, near-infrared and mid-infrared regions are alternatives for many traditional laboratory methods for measuring soil properties. However, debate exists over whether the near-infrared (700–2500 nm) or the mid-infrared (MIR, 2500–25000 nm) region of the electromagnetic spectrum is more useful for predicting soil properties. Therefore, the aim of this study was to compare UV-VIS-NIR and MIR spectroscopic techniques to predict several soil properties. A total of 415 surface and subsurface soil samples were collected from widely spread locations within New South Wales and south-eastern Queensland of Australia to model the proposed hypothesis. Principal component regression analysis (PCR) was used to develop calibration and validation models from soil spectra and reference laboratory values. The models developed using MIR spectra achieved higher prediction accuracy (regression coefficient, r2 = 0.62–0.85) for pH, organic carbon, clay, sand, CEC, and exchangeable Ca and Mg than that obtained by UV-VIS-NIR spectra (r2 = 0.28–0.76). PCR models were also developed for the combined spectral regions (UV-VIS-NIR+MIR). The models developed using combined spectra were also found to predict pH, organic carbon, clay, sand, CEC, and exchangeable Ca and Mg with acceptable accuracy (r2 = 0.59–0.79). The results of this study indicate that MIR spectra are better than UV-VIS-NIR spectra for estimation of common soil properties.

scholarly journals Mid Infrared Spectroscopy and Multivariate Analysis Evaluation of Adulteration in Whey Protein Powder

2020 ◽  
Vol 2 (1) ◽  
pp. 1-18
Author(s):  
Virgilio De Carvalho dos Anjos
Keyword(s):  
Whey Protein ◽  
Prediction Models ◽  
Principal Component ◽  
Whey Protein Concentrate ◽  
Mid Infrared ◽  
Mir Spectroscopy ◽  
Total Variability ◽  
Relative Errors ◽  
Analytical Tools

Mid infrared (MIR) spectroscopy was combined with multivariate approaches Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression to assess modifications in spectral profile of whey protein concentrate (WPC) powder due to changes in formulation level using caffeine, creatine and lactose, simulating fraud. Adulterations were made by replacing WPC in different levels from 5 to 50% (w/w - 5% steps) with three adulterants in separate. The spectra comparison of the samples allowed the identification of peaks associated to characteristic chemical bonds of each adulterant. PCA was carried out and 89% of the total variability of the spectral data was explained by three principal components, which allowed the confirmation of variables influencing each sample mixture and validating the spectral observations. Above 20% decrease in WPC content (20% adulteration), it was possible to differentiate all the three substances used. Predictions of percentage of WPC substitution were made through PLS regressions. The best prediction models were: lactose > creatine > caffeine. However, predictions resulted in overall good accuracy, low relative errors and coefficients of determination of fitting of calibration and validation curves above 0.97 in all cases. Therefore, techniques employed here aid the quality assessment of food products as alternative analytical tools.  

scholarly journals Main Human Urinary Metabolites after Genipap (Genipa americana L.) Juice Intake

Nutrients ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1155 ◽  
Author(s):  
Livia Dickson ◽  
Mathieu Tenon ◽  
Ljubica Svilar ◽  
Pascale Fança-Berthon ◽  
Raphael Lugan ◽  
...  
Keyword(s):  
Bioactive Compounds ◽  
Metabolic Pathways ◽  
High Resolution Mass Spectrometry ◽  
Principal Component ◽  
Urinary Metabolites ◽  
Human Consumption ◽  
Healthy Male ◽  
Wide Range ◽  
Before And After ◽  
First Time

Genipap (Genipa americana L.) is a native fruit from Amazonia that contains bioactive compounds with a wide range of bioactivities. However, the response to genipap juice ingestion in the human exposome has never been studied. To identify biomarkers of genipap exposure, the untargeted metabolomics approach in human urine was applied. Urine samples from 16 healthy male volunteers, before and after drinking genipap juice, were analyzed by liquid chromatography–high-resolution mass spectrometry. XCMS package was used for data processing in the R environment and t-tests were applied on log-transformed and Pareto-scaled data to select the significant metabolites. The principal component analysis (PCA) score plots showed a clear distinction between experimental groups. Thirty-three metabolites were putatively annotated and the most discriminant were mainly related to the metabolic pathways of iridoids and phenolic derivatives. For the first time, the bioavailability of genipap iridoids after human consumption is reported. Dihydroxyhydrocinnamic acid, (1R,6R)-6-hydroxy-2-succinylcyclohexa-2,4-diene-1-carboxylate, hydroxyhydrocinnamic acid, genipic acid, 12-demethylated-8-hydroxygenipinic acid, 3(7)-dehydrogenipinic acid, genipic acid glucuronide, nonate, and 3,4-dihydroxyphenylacetate may be considered biomarkers of genipap consumption. Human exposure to genipap reveals the production of derivative forms of bioactive compounds such as genipic and genipinic acid. These findings suggest that genipap consumption triggers effects on metabolic signatures.

scholarly journals Mid-Infrared Spectroscopy Supports Identification of the Origin of Organic Matter in Soils

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 215
Author(s):  
Michał Dudek ◽  
Cezary Kabała ◽  
Beata Łabaz ◽  
Paweł Mituła ◽  
Magdalena Bednik ◽  
...  
Keyword(s):  
Organic Matter ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Unknown Origin ◽  
Mid Infrared ◽  
Mir Spectroscopy ◽  
Buried Soil ◽  
Mir Spectra ◽  
Substantial Interest ◽  
Paleoecological Reconstructions

Spectroscopic methods combined with statistics have recently gathered substantial interest in pedological studies. Near-infrared (NIR) spectroscopy has been utilized, for example, for reconstructions of the history and transformations of Chernozems, although no similar research was conducted based on mid-infrared (MIR). In this paper, the relevance of MIR spectroscopy was tested in studies on the origin/affinity of organic matter from chernozemic soils. Samples collected from three vegetation classes (grasslands, forests and arable lands) were investigated using MIR spectroscopy in order to create a statistical model, which was applied on buried profiles of unknown origin. The results showed a clear disjunction of vegetation classes. Samples of buried soil were placed in the space between all classes, indicating the relation to variable vegetation. Therefore, arable lands should not be omitted in paleoecological reconstructions, because we cannot exclude the cultivation of fertile soils before their burial. It was concluded that MIR methods may have similar applicability to NIR spectroscopy. Additionally, MIR spectra may also be discriminated according to the recognized soil type, which allows for direct reconstructions of the transformation trends in buried profiles.

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