Evaluation of the Cultivated Mushroom Pleurotus ostreatus Basidiocarps Using Vibration Spectroscopy and Chemometrics

Fruiting bodies (basidiocarps) of the cultivated mushroom Pleurotus ostreatus (16 strains) were characterized by vibration spectroscopy and chemometrics. According to organic elemental analysis and Megazyme assay, the basidiocarps contained ~6.2–17.5% protein and ~18.8–58.2% total glucans. The neutral sugar analysis confirmed that glucose predominated in all the samples (~71.3–94.4 mol%). Fourier-transformed (FT) mid- and near-infrared (FT MIR, FT NIR) and FT Raman spectra of the basidiocarps were recorded, and the characteristic bands of proteins, glucans and chitin were assigned. The samples were discriminated based on principal component analysis (PCA) of the spectroscopic data in terms of biopolymeric composition. The partial least squares regression (PLSR) models based on first derivatives of the vibration spectra were obtained for the prediction of the macromolecular components, and the regression coefficients R2 and root mean square errors (RMSE) were calculated for the calibration (cal) of proteins (R2cal 0.981–0.994, RMSEcal ~0.3–0.5) and total glucans (R2cal 0.908–0.996, RMSEcal ~0.6–3.0). According to cross-validation (CV) diagnosis, the protein models were more precise and accurate (R2cv 0.901–0.970, RMSEcv ~0.6–1.1) than the corresponding total glucan models (R2cv 0.370–0.804, RMSEcv ~4.7–8.5) because of the wide structural diversity of these polysaccharides. Otherwise, the Raman band of phenylalanine ring breathing vibration at 1004 cm−1 was used for direct quantification of proteins in P. ostreatus basidiocarps (R ~0.953). This study showed that the combination of vibration spectroscopy with chemometrics is a powerful tool for the evaluation of culinary and medicinal mushrooms, and this approach can be proposed as an alternative to common analytical methods.

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Rapid Authentication and Quality Evaluation of Cinnamomum verum Powder Using Near-Infrared Spectroscopy and Multivariate Analyses

Planta Medica ◽

10.1055/a-0654-5468 ◽

2018 ◽

Vol 84 (18) ◽

pp. 1380-1387 ◽

Cited By ~ 6

Author(s):

Eman Shawky ◽

Dina Selim

Keyword(s):

Near Infrared ◽

Principal Component ◽

Hierarchical Cluster ◽

Global Market ◽

Least Squares Regression ◽

Time Saving ◽

Quality Aspects ◽

Counterfeit Pharmaceuticals ◽

Analysis Models ◽

Mean Square Errors

AbstractParallel to the growing global interest in alternative medical therapies, high measures of counterfeit pharmaceuticals enter the global market and, therefore, detection of such marketed products is essential. This article throws an illuminating spot on the adulteration of Cinnamomum verum (Cinnamomum zeylanicum) with Cinnamomum cassia and exhaustively extracted C. verum. A speedy and nondestructive near-infrared method in conjunction with the mathematical tools of chemometrics was used to distinguish between genuine cinnamon and its common adulterants. The principal component analysis and the hierarchical cluster analysis models successfully discriminated between unadulterated and adulterated samples. In the second part of the work, soft independent modeling of class analogy was implemented to construct a chemometric model to authenticate C. verum samples. The constructed model could successfully predict and judge the quality of C. verum powder without any misleading predictions. Finally, partial least squares regression was approached to establish the correlation for adulterated samples regarding their cassia and exhausted cinnamon content. The R2 of calibration and validation were all higher than 0.9, while the root mean square errors were all lower than 0.05, indicating that the established models were successful. Overall, the developed models were shown to have significant potential as time-saving and accurate methods for identification of true cinnamon powder, which can help guarantee both quality aspects of identity and purity of the herbal drug by avoiding its adulteration and could be implemented as a routine screening in its quality control with no need for any sample preparation.

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Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds

Animal Production Science ◽

10.1071/an17240 ◽

2019 ◽

Vol 59 (6) ◽

pp. 1190 ◽

Cited By ~ 2

Author(s):

A. Bahri ◽

S. Nawar ◽

H. Selmi ◽

M. Amraoui ◽

H. Rouissi ◽

...

Keyword(s):

Principal Component Analysis ◽

Infrared Spectroscopy ◽

Random Forest ◽

Near Infrared Spectroscopy ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Principal Component ◽

Least Squares Regression ◽

Random Forest Regression ◽

Milk Samples

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.

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Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

Sensors ◽

10.3390/s18113855 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3855 ◽

Cited By ~ 9

Author(s):

Lin Bai ◽

Cuizhen Wang ◽

Shuying Zang ◽

Changshan Wu ◽

Jinming Luo ◽

...

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Soil Ph ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Hyperspectral Data ◽

Validation Dataset ◽

Least Squares Regression ◽

Songnen Plain ◽

Mean Square Errors

In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China.

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Fungal decay of spruce and beech wood assessed by near-infrared spectroscopy in combination with uni- and multivariate data analysis

Holzforschung ◽

10.1515/hf.2007.098 ◽

2007 ◽

Vol 61 (6) ◽

pp. 680-687 ◽

Cited By ~ 26

Author(s):

Karin Fackler ◽

Manfred Schwanninger ◽

Cornelia Gradinger ◽

Ewald Srebotnik ◽

Barbara Hinterstoisser ◽

...

Keyword(s):

Data Analysis ◽

Multivariate Data Analysis ◽

Near Infrared ◽

Lignin Content ◽

Beech Wood ◽

Multivariate Data ◽

Principal Component ◽

Brown Rot ◽

White Rot ◽

Least Squares Regression

Abstract Wood is colonised and degraded by a variety of micro-organisms, the most efficient ones are wood-rotting basidiomycetes. Microbial decay processes cause damage to wooden constructions, but also have great potential as biotechnological tools to change the properties of wood surfaces and of sound wood. Standard methods to evaluate changes in infected wood, e.g., EN350-1 1994, are time-consuming. Rapid FT-NIR spectroscopic methods are also suitable for this purpose. In this paper, degradation experiments on surfaces of spruce (Picea abies L. Karst) and beech (Fagus silvatica L.) were carried out with white rot basidiomycetes or the ascomycete Hypoxylon fragiforme. Experiments with brown rot or soft rot caused by Chaetomium globosum were also performed. FT-NIR spectra collected from the degraded wood were subjected to principal component analysis. The lignin content and mass loss of the specimens were estimated based on univariate or multivariate data analysis (partial least squares regression).

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Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy

Canadian Journal of Soil Science ◽

10.4141/s01-054 ◽

2002 ◽

Vol 82 (4) ◽

pp. 413-422 ◽

Cited By ~ 64

Author(s):

P D Martin ◽

D F Malley ◽

G. Manning ◽

L. Fuller

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Analytical Data ◽

Principal Component ◽

Soil Samples ◽

Organic N ◽

Least Squares Regression ◽

Total N ◽

Organic C

This study explored the use of near-infrared spectroscopy (NIRS) for the rapid analysis of organic C (Corg) and organic N (Norg) in the A horizon of soil within a single field. Soil was sampled throughout a field in Manitoba, Canada to capture soil variability associated with topography. The soil samples were oven-dried and treated with acid to remove carbonates, after which C and N were determined by dry combustion. In this study, portions of the dried soil samples not treated with acid were scanned with a near-infrared scanning spectrophotometer between 1100 and 2500 nm. Correlating the spectral and the chemical analytical data using multiple linear regression or principal component analysis/partial least squares regression gave useful correlations for Corg. Over the range of 0–40 mg g-1 Corg, NIR-predicted values explained 75–78% of the variance in the chemical results. Results were improved to 80% for calibrations developed for the 0–20 mg g-1 organic C range. Useful results were not obtained for Norg although the literature shows that total N in soil is predictable using NIRS. It is likely that the acid treatment altered the composition of the samples in an inconsistent manner such that the chemically analyzed samples and those scanned by NIRS were different from each other in Norg concentration or composition. Extrapolation of these Corg results to the landscape scale implies that NIRS has potential to be a suitable method for mapping C for the purposes of monitoring C sequestration. Key words: Near-infrared spectroscopy, soil, carbon, nitrogen, topography, soil monitoring

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DWT-PLS Regression on Near-Infrared Spectra for Moisture Determination of Coal

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.827.209 ◽

2013 ◽

Vol 827 ◽

pp. 209-212

Author(s):

Xiao Li Yang ◽

Fan Wang ◽

Wen Chao Wang ◽

Yun Xiu Chen ◽

Ji Shu Chen

Keyword(s):

Bituminous Coal ◽

Near Infrared ◽

Principal Component ◽

Discrete Wavelet ◽

Moisture Determination ◽

Least Squares Regression ◽

Compression Performance ◽

Moisture Analysis ◽

Near Infrared Spectra ◽

Compressed Data

We studied moisture determination in bituminous coal and lignitic coal samples using near-infrared (NIR) spectra. This research was developed by applying partial least squares regression (PLS) and discrete wavelet transform (DWT). Firstly, the NIR spectra were pre-processed by DWT for fitting and compression. Then, the compressed data were used to build regression model with PLS for moisture determination in coal samples. Compression performance at different resolution scales was investigated. Using the compressed data, PLS can obtain more accurate result than using raw spectra. The number of principal component in PLS model was investigated too. The results show DWT-PLS can obtain satisfactory determination performance for moisture analysis in bituminous coal and lignitic coal.

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Application of Near-Infrared Spectroscopy to Investigate Some Endogenic Properties of Pleurotus ostreatus Cultivars

Sensors ◽

10.3390/s20226632 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6632

Author(s):

Marietta Fodor ◽

Erika Etelka Mikola ◽

András Geösel ◽

Éva Stefanovits-Bányai ◽

Zsuzsanna Mednyánszky

Keyword(s):

Amino Acid ◽

Free Amino Acid ◽

Pleurotus Ostreatus ◽

Free Amino ◽

Near Infrared ◽

Amino Acid Content ◽

Principal Component ◽

Automatic Amino Acid Analyzer ◽

Linear Discriminant ◽

Reducing Ability

Fourteen different Pleurotus ostreatus cultivars (Po_1–Po_14) were tested for free amino acid content (fAA), total polyphenol content (TPC), and antioxidant capacity (Ferric Reducing Ability of Plasma—FRAP) to select the cultivars with the most favorable traits. Automatic amino acid analyzer (fAA) and spectrophotometric assay (TPC, FRAP) results as well as Fourier-transform near infrared (FT-NIR) spectra were evaluated with different chemometric methods (Kruskal–Wallis test, Principal Component Analysis—PCA, Linear Discriminant Analysis—LDA). Based on total free amino acid concentrations and FRAP values, the Po_2 cultivar was found to be the most favorable. Types Po_3, Po_8, Po_10 and Po_12 were separated using PCA. Based on the spectral profile, they may contain polyphenols and reducing compounds of different qualities. LDA classification that was based on the concentrations of all free amino acids, cysteine, and proline of the cultivars was performed with an accuracy of over 90%. LDA classification that was based on the TPC and FRAP values was performed with an accuracy of over 83%.

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NIR-chemometric approaches for evaluating carbonization characteristics of hydrothermally carbonized lignin

Scientific Reports ◽

10.1038/s41598-021-96461-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sung-Wook Hwang ◽

Un Taek Hwang ◽

Kyeyoung Jo ◽

Taekyeong Lee ◽

Jinseok Park ◽

...

Keyword(s):

Least Squares ◽

Infrared Spectra ◽

Regression Models ◽

Near Infrared ◽

Prediction Models ◽

Hydrothermal Carbonization ◽

Ordinary Least Squares ◽

Least Squares Regression ◽

Near Infrared Spectra ◽

Mean Square Errors

AbstractThe aim of this study is to establish prediction models for the non-destructive evaluation of the carbonization characteristics of lignin-derived hydrochars as a carbon material in real time. Hydrochars are produced via the hydrothermal carbonization of kraft lignins for 1–5 h in the temperature range of 175–250 °C, and as the reaction severity of hydrothermal carbonization increases, the hydrochar is converted to a more carbon-intensive structure. Principal component analysis using near-infrared spectra suggests that the spectral regions at 2132 and 2267 nm assigned to lignins and 1449 nm assigned to phenolic groups of lignins are informative bands that indicate the carbonization degree. Partial least squares regression models trained with near-infrared spectra accurately predicts the carbon content, oxygen/carbon, and hydrogen/carbon ratios with high coefficients of determination and low root mean square errors. The established models demonstrate better prediction than ordinary least squares regression models.

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105 NEAR-INFRARED SPECTROSCOPY AND AQUAPHOTOMICS ANALYSIS OF SERUM FROM MARES EXPOSED TO THE FUNGAL MYCOTOXIN ZEARALENONE

Reproduction Fertility and Development ◽

10.1071/rdv29n1ab105 ◽

2017 ◽

Vol 29 (1) ◽

pp. 160

Author(s):

C. K. Vance ◽

K. R. Counsell ◽

L. A. Agcanas ◽

N. Shappell ◽

S. Bowers ◽

...

Keyword(s):

Principal Component Analysis ◽

Near Infrared ◽

Principal Component ◽

Component Analysis ◽

Spectral Features ◽

Serum Chemistry ◽

Least Squares Regression ◽

Aqueous Mixtures ◽

Estrogenic Potential ◽

Spectral Profiles

Aquaphotomics is a branch of near-infrared (NIR) spectroscopy in which bond vibrations from organic molecules and water create unique spectral absorbance patterns to profile complex aqueous mixtures. Aquaphotomics has been shown to detect virus infected soybean plants from extracts, classify probiotic bacteria, and detect contamination of aquatic ecosystems. We have used aquaphotomics to characterise serum profiles from horses in various phases of the reproductive cycle such as oestrus and diestrus. Because serum is a complex solution of biomolecules, various modes of serum processing (e.g. large protein removal for proteomics or mass spectrometry) may provide different NIR spectral profiles for quantitative analysis of specific compounds or their effects. Zearalenone is a fungal mycotoxin that may have estrogenic potential in mares and is found in feedstuffs. The objectives of this study were to (1) establish NIR spectral profiles of serum and protein-precipitated serum (PPS) collected at peak oestrus from mares; (2) determine if NIR profiles correlate and quantify E2 concentrations in serum or PPS; and (3) determine if NIR can detect differences in serum chemistry of zearalenone-treated mares. Mares were fed zearalenone daily at low (2 mg, 2 mares, 5 cycles) and high (8 mg, 1 mare, 3 cycles) concentrations, plus control (0 mg, 1 mare, 3 cycles). Oestrus cycles were monitored by ultrasound and serum hormone analysis. Serum collected at peak oestrus had E2 values determined by radioimmunoassay (range 0.02–16.87 pg mL−1). Protein precipitated serum had high and medium MW proteins removed with acetonitrile. NIR spectra, collected in triplicate with a 1 mm quartz cuvette and ASD FieldSpec®3 (Boulder, CO, USA), were pre-treated with a Savitsky-Golay 1st derivative for inspection of spectral features, principal component analysis, and partial least-squares regression (PLS) to investigate spectral correlations to E2 concentrations and zearalenone treatment effects. The NIR profiles contrasting serum and PPS at oestrus had distinct spectral features differing significantly at 1320, 1491, 1536, and 1566 nm in the NIR water spectrum, and principal component-1 accounted for 97% of principal component analysis variance in spectra from serum compared to PPS. In the PLS cross-validation linear fit regression model, NIR predicted E2 concentrations (validated by RIA) from serum (slope = 0.89, SECV = 1.92, R2 = 0.81, 3 factors), and from PPS (slope = 0.61; SECV = 1.84, R2 = 0.76, 4 factors). Spectral predictions were poorest at the low E2 threshold, E2 = 0.02 pg mL−1. The PLS model validation metrics of zearalenone dose-dependent effects were also evident in serum (slope = 0.88, SECV = 1.26, R2 = 0.86) and in PPS (slope = 0.67, SECV = 1.96, R2 = 0.66). Correlations of quantitative values of E2 and zearalenone were both better for spectra taken of serum compared to PPS. In summary, NIR spectral profiles of serum chemistry may be able to map E2 hormone levels during reproductive cycling, and these spectra may also have correlations that reflect exposure of mares to estrogenic toxins such as zearalenone. Research was supported by USDA-ARS Biophotonics grant #58-6402-3-018.

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Screening of grated cheese authenticity by nir spectroscopy

Journal of Agricultural Engineering ◽

10.4081/jae.2013.295 ◽

2013 ◽

Vol 44 (2s) ◽

Cited By ~ 3

Author(s):

Chiara Cevoli ◽

Angelo Fabbri ◽

Alessandro Gori ◽

Maria Fiorenza Caboni ◽

Adriano Guarnieri

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Classification Performance ◽

Integrating Sphere ◽

Least Squares Regression ◽

Test Set ◽

Spectra Analysis ◽

Protected Designation Of Origin ◽

Parmigiano Reggiano

Parmigiano–Reggiano (PR) cheese is one of the oldest traditional cheeses produced in Europe, and it is still one of the most valuable Protected Designation of Origin (PDO) cheeses of Italy. The denomination of origin is extended to the grated cheese when manufactured exclusively from whole Parmigiano-Reggiano cheese wheels that respond to the production standard. The grated cheese must be matured for a period of at least 12 months and characterized by a rind content not over 18%. In this investigation the potential of near infrared spectroscopy (NIR), coupled to different statistical methods, were used to estimate the authenticity of grated Parmigiano Reggiano cheese PDO. Cheese samples were classified as: compliance PR, competitors, non-compliance PR (defected PR), and PR with rind content greater then 18%. NIR spectra were obtained using a spectrophotometer Vector 22/N (Bruker Optics, Milan, Italy) in the diffuse reflectance mode. Instrument was equipped with a rotating integrating sphere. Principal Component Analysis (PCA) was conducted for an explorative spectra analysis, while the Artificial Neural Networks (ANN) were used to classify spectra, according to different cheese categories. Subsequently the rind percentage and month of ripening were estimated by a Partial Least Squares regression (PLS). Score plots of the PCA show a clear separation between compliance PR samples and the rest of the sample was observed. Competitors samples and the defected PR samples were grouped together. The classification performance for all sample classes, obtained by ANN analysis, was higher of 90%, in test set validation. Rind content and month of ripening were predicted by PLS a with a determination coefficient greater then 0.95 (test set). These results showed that the method can be suitable for a fast screening of grated cheese authenticity.

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