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

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.

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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 ◽

10.3390/molecules26071879 ◽

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.

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

Molecules ◽

10.3390/molecules23123343 ◽

2018 ◽

Vol 23 (12) ◽

pp. 3343 ◽

Cited By ~ 5

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.

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The application of pattern recognition techniques in metabolite fingerprinting of six differentPhyllanthusspp.

Spectroscopy An International Journal ◽

10.1155/2011/980109 ◽

2011 ◽

Vol 26 (1) ◽

pp. 69-78 ◽

Cited By ~ 3

Author(s):

Saravanan Dharmaraj ◽

Lay-Harn Gam ◽

Shaida Fariza Sulaiman ◽

Sharif Mahsufi Mansor ◽

Zhari Ismail

Keyword(s):

Pattern Recognition ◽

Principal Component ◽

Canonical Variate ◽

Batch Mode ◽

Mid Infrared ◽

Linear Discriminant ◽

Pattern Recognition Techniques ◽

Pattern Recognition Methods ◽

Ftir Spectrometer ◽

Component Models

FTIR spectroscopy was used together with multivariate analysis to distinguish six different species ofPhyllanthus. Among these speciesP. niruri,P. debilisandP. urinariaare morphologically similar whereasP. acidus,P. emblicaandP. myrtifoliusare different. The FTIR spectrometer was used to obtain the mid-infrared spectra of the dried powdered leaves in the region of 400–4000 cm−1. The region of 400–2000 cm−1was analyzed with four different pattern recognition methods. Initially, principal component analysis (PCA) was used to reduce the spectra to six principal components and these variables were used for linear discriminant analysis (LDA). The second technique used LDA on most discriminating wavenumber variables as searched by genetic algorithm using canonical variate approach for either 30 or 60 generations. SIMCA, which consisted of constructing an enclosure for each species using separate principal component models, was the third technique. Finally, multi-layer neural network with batch mode of backpropagation learning was used to classify the samples. The best results were obtained with GA of 60 gens. When LDA was run with the six wavenumbers chosen (1151, 1578, 1134, 609, 876 and 1227), 100% of the calibration spectra and 96.3% of the validation spectra were correctly assigned.

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

Revista de Chimie ◽

10.37358/rc.18.2.6129 ◽

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.

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

Soil Research ◽

10.1071/sr04182 ◽

2005 ◽

Vol 43 (6) ◽

pp. 713 ◽

Cited By ~ 77

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.

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Fermentation Quality and Aroma Profile of Winter Cereals and Italian Ryegrass Plus Winter Cereal Mixture Silages

10.21203/rs.3.rs-466781/v1 ◽

2021 ◽

Author(s):

Alemayehu Worku Babu ◽

Tamás Tóth ◽

Szilvia Orosz ◽

Hedvig Fébel ◽

László Kacsala ◽

...

Keyword(s):

Electronic Nose ◽

Principal Component ◽

Italian Ryegrass ◽

Ethyl Esters ◽

Winter Cereal ◽

Aroma Profile ◽

Linear Discriminant ◽

Winter Cereals ◽

Fermentation Quality ◽

Mold And Yeast

Abstract During silage making microbial fermentation produces an array of end products which can influence the odour of the final silage and can also change many nutritive aspects of a forage. The objective of this study was to evaluate the fermentation quality and aroma profile of winter cereals and Italian ryegrass (Lolium multiflorum Lam., IRG) plus winter cereal mixture silages detected with an electronic nose. Four mixtures (mixture A: triticale, oats, barley and wheat; mixture B: triticale, barley and wheat; mixture C: IRG and oats; mixture D: IRG, oats, triticale, barley and wheat) were harvested, wilted and ensiled in laboratory-scale silos (n = 80) without additives. Mixture C had higher (P < 0.05) mold and yeast (Log10 CFU (colony forming unit)/g) counts compared to mixture B. Mixture B and C had higher acetic acid (AA) content than mixture A and D. The lactic acid (LA) content was higher for mixture B than mixture C. At the end of 90 days fermentation winter cereal mixture silages (mixture A and B) had similar aroma pattern, and mixture C was also similar to winter cereal silages. However, mixture D had different aromatic pattern than other ensiled mixtures. Both the principal component analysis (PCA) score plot for aroma profile and linear discriminant analysis (LDA) classification revealed that mixture D had different aroma profile than other mixture silages. The difference was caused by the presence of high ethanol and LA in mixture D. Ethyl esters such as ethyl 3-methyl pentanoate, 2-methylpropanal, ethyl acetate, isoamyl acetate and ethyl-3-methylthiopropanoate were found at different retention indices in mixture D silage. The low LA and higher mold and yeast count in mixture C silage caused off odour due to the presence of 3-methylbutanoic acid, a simple alcohol with unpleasant camphor-like odor. In general, the electronic nose (EN) results revealed that the ensiled mixtures were dominated by ethyl ester likely producing pleasant fruity odors which could increase the intake of ensiled mixtures. However, the technology is suitable in finding off odor compounds of ensiled forages that may likely reduce feed intake.

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Tracing the origin of paracetamol tablets by near-infrared, mid-infrared, and nuclear magnetic resonance spectroscopy using principal component analysis and linear discriminant analysis

Analytical and Bioanalytical Chemistry ◽

10.1007/s00216-021-03249-z ◽

2021 ◽

Author(s):

Alexander Becht ◽

Curd Schollmayer ◽

Yulia Monakhova ◽

Ulrike Holzgrabe

Keyword(s):

Principal Component Analysis ◽

Nuclear Magnetic Resonance ◽

Magnetic Resonance ◽

Pharmaceutical Company ◽

Near Infrared ◽

Principal Component ◽

Spectroscopic Methods ◽

Mid Infrared ◽

Linear Discriminant ◽

Nuclear Magnetic

AbstractMost drugs are no longer produced in their own countries by the pharmaceutical companies, but by contract manufacturers or at manufacturing sites in countries that can produce more cheaply. This not only makes it difficult to trace them back but also leaves room for criminal organizations to fake them unnoticed. For these reasons, it is becoming increasingly difficult to determine the exact origin of drugs. The goal of this work was to investigate how exactly this is possible by using different spectroscopic methods like nuclear magnetic resonance and near- and mid-infrared spectroscopy in combination with multivariate data analysis. As an example, 56 out of 64 different paracetamol preparations, collected from 19 countries around the world, were chosen to investigate whether it is possible to determine the pharmaceutical company, manufacturing site, or country of origin. By means of suitable pre-processing of the spectra and the different information contained in each method, principal component analysis was able to evaluate manufacturing relationships between individual companies and to differentiate between production sites or formulations. Linear discriminant analysis showed different results depending on the spectral method and purpose. For all spectroscopic methods, it was found that the classification of the preparations to their manufacturer achieves better results than the classification to their pharmaceutical company. The best results were obtained with nuclear magnetic resonance and near-infrared data, with 94.6%/99.6% and 98.7/100% of the spectra of the preparations correctly assigned to their pharmaceutical company or manufacturer. Graphical abstract

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Maize silage and winter crop options to maximise drymatter and energy for NZ dairy systems

Proceedings of the New Zealand Grassland Association ◽

10.33584/jnzg.2006.68.2647 ◽

2006 ◽

pp. 193-197 ◽

Cited By ~ 2

Author(s):

R.J. Densley ◽

G.M. Austin ◽

I.D. Williams ◽

R. Tsimba ◽

G.O. Edmeades

Keyword(s):

Italian Ryegrass ◽

Maize Silage ◽

Forage Crops ◽

Forage Crop ◽

Maize Hybrid ◽

Trade Offs ◽

Feed Value ◽

Winter Crop ◽

Winter Crops ◽

Silage Production

Trade-offs in dry matter (DM) and metabolisable energy (ME) between combinations of three maize silage hybrids varying in maturity from 100-113 CRM and six winter forage options were investigated in a Waikato farmer's field over 2 years. Winter crops were triticale, cut once; oats grazed 1-2 times; and Tama and Feast II Italian ryegrass, each cut or grazed 2-3 times. Greatest DM and ME production (38.9 t/ha; 396 GJ/ha) was from a 113 CRM hybrid followed by a single-cut triticale crop. The most economical sources of DM and ME were obtained from a 100 CRM maize hybrid plus grazed oats (11.8 c/ kg; 1.12 c/MJ), while the cheapest ME source among cut winter forages was a 113 CRM maize hybrid + triticale (1.18 c/MJ). Reliable annual silage production of 30 t DM/ha and 330 GJ ME/ha (or 3000 kg MS/ha) is possible using a late maturing maize hybrid combined with a winter forage crop such as triticale, although the low feed value of the triticale may limit its use as feed for milking cows. Keywords: Italian ryegrass, oats, maize silage, supplements, triticale, winter forage crops

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Time lapse synchrotron IR chemical imaging for observing the acclimation of a single algal cell to CO2 treatment

Scientific Reports ◽

10.1038/s41598-021-92657-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ghazal Azarfar ◽

Ebrahim Aboualizadeh ◽

Simona Ratti ◽

Camilla Olivieri ◽

Alessandra Norici ◽

...

Keyword(s):

Algal Cell ◽

Principal Component ◽

External Perturbation ◽

Time Lapse ◽

Chemical Imaging ◽

Hyperspectral Data ◽

Aqueous Environment ◽

Ir Absorption ◽

Imaging Data ◽

Mid Infrared

AbstractAlgae are the main primary producers in aquatic environments and therefore of fundamental importance for the global ecosystem. Mid-infrared (IR) microspectroscopy is a non-invasive tool that allows in principle studying chemical composition on a single-cell level. For a long time, however, mid-infrared (IR) imaging of living algal cells in an aqueous environment has been a challenge due to the strong IR absorption of water. In this study, we employed multi-beam synchrotron radiation to measure time-resolved IR hyperspectral images of individual Thalassiosira weissflogii cells in water in the course of acclimation to an abrupt change of CO2 availability (from 390 to 5000 ppm and vice versa) over 75 min. We used a previously developed algorithm to correct sinusoidal interference fringes from IR hyperspectral imaging data. After preprocessing and fringe correction of the hyperspectral data, principal component analysis (PCA) was performed to assess the spatial distribution of organic pools within the algal cells. Through the analysis of 200,000 spectra, we were able to identify compositional modifications associated with CO2 treatment. PCA revealed changes in the carbohydrate pool (1200–950 cm$$^{-1}$$ - 1 ), lipids (1740, 2852, 2922 cm$$^{-1}$$ - 1 ), and nucleic acid (1160 and 1201 cm$$^{-1}$$ - 1 ) as the major response of exposure to elevated CO2 concentrations. Our results show a local metabolism response to this external perturbation.

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Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes

Metabolites ◽

10.3390/metabo11050265 ◽

2021 ◽

Vol 11 (5) ◽

pp. 265

Author(s):

Ruchi Sharma ◽

Wenzhe Zang ◽

Menglian Zhou ◽

Nicole Schafer ◽

Lesa A. Begley ◽

...

Keyword(s):

Gas Chromatography ◽

Real Time ◽

Rapid Assessment ◽

Principal Component ◽

Corticosteroid Treatment ◽

Breath Analysis ◽

Blood Eosinophil ◽

Exhaled Breath ◽

Respiratory Illnesses ◽

Linear Discriminant

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

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