Fast Classification of Different Oils and Routes Used in Biodiesel Production Using Mid Infrared Spectroscopy and PLS2-DA

Evaluation of multiblock NIR/MIR PLS predictive models to detect adulteration of diesel/biodiesel blends by vegetal oil

Mid infrared spectroscopy and multivariate analysis: A tool to discriminate between organic and non-organic wines grown in Australia

The applicability of near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was explored in this study to develop rapid, low-cost and non-destructive spectroscopic methods for classification and quantification of aflatoxins in brown rice. A total of 132 brown rice samples within the aflatoxin concentration range of 0-2435.8μg/kg were prepared by artificially inoculated with A. flavus and A. parasiticus strains of fungus. For the classification of samples at varying levels of aflatoxin B1, the linear discriminant analysis model obtained correct classification rate of 96.9 and 90.6% for NIR and MIR spectroscopy, respectively. For the simultaneous determination of aflatoxins B1, B2, G1, G2 and the total aflatoxins, partial least squares regression also showed good predictive accuracy for both NIR (rv =0.936-0.973, RPD=2.5-4.0) and MIR spectroscopy (rv =0.922-0.970, RPD=2.5-4.0). The overall results indicated that the two spectroscopic techniques offered the feasibility to be used as alternative tools for rapid detection of various aflatoxin contaminations in grain.

The mid-infrared spectroscopy: A novel non-invasive diagnostic tool for NASH diagnosis in severe obesity

Predicting the decomposability of arctic tundra soil organic matter with mid infrared spectroscopy

In situ and laboratory soil spectroscopy with portable visible-to-near-infrared and mid-infrared instruments for the assessment of organic carbon in soils

Baijiu is a traditional and popular Chinese liquor which is affected by the storage time. The longer the storage time of Baijiu is, the better its quality is. In this paper, the raw and mellow Baijiu samples from different storage time are discriminated accurately throughout midinfrared (MIR) spectroscopy and chemometrics. Firstly, changing regularities of the substances in Chinese Baijiu are discussed by gas chromatography-mass spectrometry (GC-MS) during the aging process. Then, infrared spectrums of Baijiu samples are processed by smoothing, multivariate baseline correction, and the first and second derivative processing, but no significant variation can be observed. Next, the spectral date pretreatment methods are constructively introduced, and principal component analysis (PCA) and discriminant analysis (DA) are developed for data analyses. The results show that the accuracy rates of samples by the DA method in calibration and validation sets are 91.7% and 100%, respectively. Consequently, an identification model based on support vector machine (SVM) and PCA is established combined with the grid search strategy and cross-validation methods to discriminate the age of Chinese Baijiu validly, where 100% classification accuracy rate is obtained in both training and test sets.

The use of diffuse reflectance mid-infrared spectroscopy for the prediction of the concentration of chemical elements estimated by X-ray fluorescence in agricultural and grazing European soils

Applying NIR and MIR spectroscopy for C and soil property prediction in northern cold-region ecosystems. Which approach works better?

The fermented seed of néré (Parkia biglobosa), known as Soumbara, is a condiment widely consumed by the population in West Africa and Guinea in particular. The present study aims to use physicochemical, colorimetric methods and mid-infrared (MIR) spectroscopy to evaluate the evolution of the quality of Soumbara during storage (30 days). Thus, 5 kg of Soumbara were taken, subdivided into 7 batches of samples, and analysed every 5 days for 30 days. Physicochemical analyses (water activity, moisture, proteins, and total ash) would show that the storage duration induced a variation during storage; for example, the total ash contents on days 1, 5, 10, 15, 20, 25 and 30 are respectively 3.96 ± 0.25, 3.89 ± 0.43, 3.97 ± 0, 23, 3.99 ± 0.12, 4.54 ± 0.14, 4.57 ± 0.2 and 4.8 ± 0.21%. During the first 15 days of storage, no significant differences (p < 0.05) were observed between samples. The colorimetric measurements showed that the storage duration did not significantly affect the values of a* and b*, except the L* value which decreased significantly (p<0.05) (42.16 ± 0.015 on day 1 to 40.02 ± 0.015 on day 30). Applying statistical methods to mid-infrared spectroscopy allowed a clear distinction between the Soumbara samples during storage for 30 days. The partial least squares regression (PLSR) applied to MIR spectra made it possible to predict water, moisture and protein activity levels, since R2 greater than 0.99 were obtained. The results demonstrated that mid-infrared spectroscopy coupled with chemometric tools could be used as a rapid screening tool to assess and monitor the quality of Soumbara during storage.

In the last decade, near-infrared (NIR) and mid-infrared (MIR) spectroscopy have proven to be most promising tools for the detection of fungal contamination and estimation of mycotoxins in agricultural commodities, particularly of cereals. Owing to significant economic losses incurred from fungal contamination of foodstuffs, producers and processors are looking for fast, reliable, and less-expensive methods for the detection of fungal damage. In this context, NIR and MIR spectroscopy offer a fast, less-expensive, non-destructive, and relatively simple analytical method. Results from published studies indicate that NIR and MIR spectroscopy can be successfully applied to identifying fungal contamination and estimating specific mycotoxins. This review will focus on the applications of NIR and MIR spectroscopy to the classification of fungal contamination and the determination of specific mycotoxin contamination levels, and to compare this technology with traditional analytical methods.

Salts in the root zone have high spatial variability, change rapidly and adversely affect soil quality and crop productivity. In contrast to the time-intensive traditional methods for measuring electrical conductivity (EC), visible-near infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy provide faster alternatives that can assist in creating strategies to reduce negative impacts on soil and plants. Soils were collected from the Indo-Gangetic Plains and analysed for EC1:2.5 using conventional method. There was a wide variation in EC measured by the conventional method. So Partial Least Squares Regression (PLSR) was used to predict soil EC from spectral data, with the data divided into calibration (70%) and validation (30%) datasets. The partial least square regression (PLSR), random forest (RF), support vector regression (SVR) and multivariate adaptive regression splines (MARS) both in Vis-NIR and MIR region during calibration. The predictive performance of PLSR, RF, SVR, and MARS models for EC1:2.5 in the Vis-NIR range showed PLSR as the best model (R2 = 0.84, RMSE = 0.21, RPD = 2.44). In the MIR range, RF was considered fairly good (R2 = 0.52, RMSE = 0.20, RPD = 1.43). Vis-NIR spectroscopy with PLSR algorithm predicted EC better than MIR spectroscopy and would be the method of choice for rapid estimation and prediction of EC in the study region.

Molecular structure modifications of camel milk (CaM) and cow milk (CM) mixtures during coagulation were investigated combining mid-infrared (MIR) spectroscopic monitoring with Parallel Factor Analysis (PARAFAC) and particle-size measurements. To evaluate the structure evolution at the molecular level, five different milk formulations were prepared using the following volume fractions of CaM in the mixtures: 100, 75, 50, 25, and 0. Regarding MIR spectroscopy, wavelength ranges located between 3,000 and 2,800 cm−1 corresponding to fatty acids; from 1,700 to 1,500 cm−1, related to amide I and II bands; and in the 1,500–900 cm−1 range called the fingerprint region were considered for the characterization of milk coagulation kinetics. MIR spectroscopy with PARAFAC was allowed for the identification of modifications at the molecular level depending on the coagulation time and milk composition. This was also confirmed by canonical correlation analysis, which demonstrated a high degree of correlation between the casein particle-size distributions and MIR spectra measured during coagulation. Novelty impact statement PARAFAC coupled with MIR spectroscopy were successfully used to monitor coagulation of CM, CaM, and their mixtures (CaM, CM, 1CaM:1CM, 1CaM:3CM, 3CaM:CM1; v/v). PARAFAC coupled with MIR spectroscopy were successfully used to differentiate between physicochemical phenomenon occurring during coagulation of different mixtures of CM, CaM and their mixtures. Canonical correlation analysis (CCA) of MIR spectra and particle-size distribution during milk coagulation demonstrated a strong relationship between the formation of casein aggregates and the variation of the MIR spectral data.

Towards personalised saliva spectral fingerprints: Comparison of mid infrared spectra of dried and whole saliva samples

Quantitative analysis of caprolactam in sauce-based food using infrared spectroscopy combined with data fusion strategies