Fourier Transform Mid-infrared Spectroscopy Research Articles

Fourier Transform Mid-Infrared (FTIR) Spectroscopy to Identify Tannin Compounds in The Panicle of Sorghum Mutant Lines

G5 and G8 are two sorghum mutant lines projected as sorghum varieties for ruminant’s feed. However, most of the sorghum panicles contains phenolic compounds, especially in the form of condensed tannins. The objective of this research was to investigate the phenolic and tannin compounds from the panicles of two sorghum mutant lines. The second objective was to identify tannin content by Fourier Transform Mid-Infrared (FTIR) Spectroscopy in the sorghum panicles. The two mutant lines were compared with Pahat variety. This research was arranged into a randomized complete block design with two factors. The first factor was the sorghum variety/mutant lines, whereas the second factor was the generative phase of harvesting time (flowering, soft dough and hard dough). The results showed that the panicles of G5 sorghum contained the lowest total phenol compounds (P<0.01). The average mean of G5 and G8 mutant lines produced lower total tannin content (27.3 and 18.2%, respectively) than Pahat variety (P<0.01). Generally, total phenol and total tannin in all variety/mutant lines increased with the increasing maturity stage (P<0.01). All the spectra of the sorghum panicle samples in different harvesting age investigated shared certain special similarities. A band due to alkane medium (C-H) stretch was observed at 2937-2854 cm−1. At 1521-1514 and 1446-1408 cm−1, a band due to the C-C aromatic compounds was observed. Based on those findings, it can be concluded that G5 and G8 mutant lines produced less total phenol and total tannin contents than Pahat variety. Meanwhile, FTIR could be used to describe the profile of tannin compounds qualitatively.

Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle

The use of Fourier-transform mid-infrared (FTIR) spectroscopy is of interest to the dairy industry worldwide for predicting milk composition and other novel traits that are difficult or expensive to measure directly. Although there are many valuable applications for FTIR spectra, noise from differences in spectral responses between instruments is problematic because it reduces prediction accuracy if ignored. The purpose of this study was to develop strategies to reduce the impact of noise and to compare methods for standardizing FTIR spectra in order to reduce between-instrument variability in multiple-instrument networks. Noise levels in bands of the infrared spectrum caused by the water content of milk were characterized, and a method for identifying and removing outliers was developed. Two standardization methods were assessed and compared: piecewise direct standardization (PDS), which related spectra on a primary instrument to spectra on 5 other (secondary) instruments using identical milk-based reference samples (n = 918) analyzed across the 6 instruments; and retroactive percentile standardization (RPS), whereby percentiles of observed spectra from routine milk test samples (n = 2,044,094) were used to map and exploit primary- and secondary-instrument relationships. Different applications of each method were studied to determine the optimal way to implement each method across time. Industry-standard predictions of milk components from 2,044,094 spectra records were regressed against predictions from spectra before and after standardization using PDS or RPS. The PDS approach resulted in an overall decrease in root mean square error between industry-standard predictions and predictions from spectra from 0.190 to 0.071 g/100 mL for fat, from 0.129 to 0.055 g/100 mL for protein, and from 0.143 to 0.088 g/100 mL for lactose. Reductions in prediction error for RPS were similar but less consistent than those for PDS across time, but similar reductions were achieved when PDS coefficients were updated monthly and separate primary instruments were assigned for the North and South Islands of New Zealand. We demonstrated that the PDS approach is the most consistent method to reduce prediction errors across time. We also showed that the RPS approach is sensitive to shifts in milk composition but can be used to reduce prediction errors, provided that secondary-instrument spectra are standardized to a primary instrument with samples of broadly equivalent milk composition. Appropriate implementation of either of these approaches will improve the quality of predictions based on FTIR spectra for various downstream applications.

The effect of anaerobic digestate derived composts on the metabolite composition and thermal behaviour of rosemary

The study reports on the effect of anaerobic digestate derived composts on the metabolite composition and thermal behaviour of rosemary (Rosmarinus officinalis L.). Plants were cultivated in semiarid soil under four different fertiliser treatments (composts of anaerobic digested cattle (C) or pig slurry (P) at 30t/ha and 60 t/ha, and two control treatments (inorganic fertiliser and no fertiliser application). Samples of leaves and stems were analysed to investigate the effect of treatment on chemical composition and thermochemical properties. Three orthogonal analytical approaches were used, namely: Fourier transform mid infrared spectroscopy (FTIR), gas chromatography/mass spectrometry (GC/MS) and thermochemical gravimetric analysis (TGA). FTIR and GC/MS showed fertiliser treatment resulted in tissue specific changes in sample metabolite composition. Fertiliser treatment was detected to change the thermogravimetric properties of the leaf samples and from inorganic and composted pig slurry digestate treatments had greater ash content and lower proportions of fixed carbon compared with samples from the unfertilised control treatment. This study provides information on how the composition of rosemary might be altered by fertiliser application in regions of poor soil, and has implications for biomass quality when rosemary is grown on semi-wild sites for the purpose of soil improvement.

Non-invasive identification of paint binders in illuminated manuscripts by ER-FTIR spectroscopy: a systematic study of the influence of different pigments on the binders\u2019 characteristic spectral features

This paper presents the results of a study of pigment-binder systems painted on parchment, both in the form of reference samples prepared in the laboratory, and of medieval and Renaissance illuminated manuscripts, by means of contactless Fourier-transform mid-infrared spectroscopy (FTIR). The research is aimed at validating external reflection FTIR (ER-FTIR) as a non-invasive method for the identification of paint binders in works of art on parchment. Based on a systematic FTIR investigation undertaken both in attenuated total reflection (ATR) and ER mode, we discuss the possibility to effectively discriminate between different binders, and the way in which the presence of a number of pigments can modify the spectral features of the pigment-binder system. The study reveals that gum Arabic can usually be discriminated from egg-based binders such as egg yolk and egg white, both in paintouts and in real artworks. Amongst all the pigments investigated, carbonates such as azurite and lead white are those that most significantly hinder the correct identification of the paint medium.

Data fusion approaches in spectroscopic characterization and classification of PDO wine vinegars

Spain is one of the major producers of high-quality wine vinegars having three protected designations of origin (a.k.a. PDOs): “Vinagre de Jerez”, “Vinagre de Condado de Huelva” and “Vinagre de Montilla-Moriles”. Their high prices due to their high quality and their high production costs explain the need for developing an adequate quality control technique and the interest in extensive characterization in order to capture the identity of each denomination. In this framework, methodologies based on non-targeted techniques, such as spectroscopies, are becoming popular in food authentication. Thus, for improving vinegar quality assessment, fusion of data blocks obtained from the same samples but different analytical techniques could be a good strategy, since the quantity and quality of sample knowledge could be enhanced providing new insights into the differentiation of vinegars. Therefore, the aim of this manuscript is the development of a multi-platform methodology and a model able to classify the Spanish wine vinegar PDOs. Sixty-five PDO wine vinegars were analyzed by four spectroscopic techniques: Fourier-transform mid-infrared spectroscopy (MIR), near infrared spectroscopy (NIR), multidimensional fluorescence spectroscopy (EEM) and proton nuclear magnetic resonance (1H-NMR). Two different data fusion strategies were evaluated: Mid-level data fusion with different preprocessing, and Common Component and Specific Weights analysis multiblock method. Exploratory and classification analysis on the data from individual techniques were also performed and compared with data fusion models. The data fusion models improved the classification, providing a more efficient differentiation, than the models based on single methods, and supporting the approach to combine these methods to achieve synergies for an optimized PDO differentiation.

Short communication: Fourier-transform mid-infrared spectroscopy to predict coagulation and acidity traits of sheep bulk milk

Sheep milk is mainly transformed into cheese; thus, the dairy industry seeks more rapid and cost-effective methods of analysis to determine milk coagulation and acidity traits. This study aimed to assess the feasibility of Fourier-transform mid-infrared spectroscopy to determine milk coagulation and acidity traits of sheep bulk milk and to classify milk samples according to their renneting capacity. A total of 465 bulk milk samples collected in 140 single-breed flocks of Comisana (84 samples, 24 flocks) and Sarda (381 samples, 116 flocks) breeds located in Central Italy were analyzed for coagulation properties (rennet coagulation time, curd firming time, and curd firmness) and acidity traits (pH and titratable acidity) using standard laboratory procedures. Fourier-transform mid-infrared spectroscopy prediction models for these traits were built using partial least squares regression analysis and were externally validated by randomly dividing the full data set into a calibration set (75%) and a validation set (25%). The discriminant capacity of the rennet coagulation time prediction model was determined using partial least squares discriminant analysis. Prediction models were more accurate for acidity traits than for milk coagulation properties, and the ratio of prediction to deviation ranged from 1.01 (curd firmness) to 2.14 (pH). Moreover, the discriminant analysis led to an overall accuracy of 74 and 66% for the calibration and validation sets, respectively, with greater sensitivity for samples that coagulated between 10 and 20 min and greater specificity to detect early-coagulating (<10 min) and late-coagulating (20-30 min) samples. Results suggest that Fourier-transform mid-infrared spectroscopy has the potential to help the dairy sheep industry identify milk with better coagulation ability for cheese production and thus improve milk transformation efficiency. However, further research is needed before this information can be exploited at the industry level.

Quantitative Analysis by HPLC and FT-MIR Prediction of Individual Sugars from the Plum Fruit Harvested during Growth and Fruit Development

Individual sugars were analyzed by High Performance Liquid Chromatography (HPLC) in plum juice samples obtained from “Stanley”, “Vânăt de Italia”, and “Tuleu Gras”. The samples were harvested from the inside and periphery of the crown during six development phases. The aim of the present study was to determine the individual sugars in plum juice by the HPLC and Fourier transform mid-infrared spectroscopy (FT-MIR) methods and to validate them. Our findings were as follows: levels between 0.26–3.73% for fructose, 1.43–1.10% for glucose, and 0.01–10.19% for sucrose. The FT-MIR analyses confirmed the differences between the juice samples, mainly in the spectral region characteristic to sugars which ranged from 900 to 1500 cm−1. FT-MIR coupled with Partial least squares (PLS) was used to develop calibration models for estimating individual plum juice sugars, the predictive characteristics being assessed. The optimal regions and spectral pretreatments were 800–1600 cm−1 and Savitzky Golay first derivative (d1) for fructose, 800–1600 cm−1 for glucose, and 915–1145 cm−1 and second derivative (d2) for sucrose. The selection of optimal spectral zones and pre-treatments led to PLS calibration models with acceptable predictive abilities for glucose, very good for sucrose, and less satisfactory for fructose. Moreover, the FT-MIR results were compared to the HPLC results in external validation tests and reference values included.

Showcasing the application of synchrotron-based X-ray computed tomography in host-pathogen interactions: The role of wheat rachilla and rachis nodes in Type-II resistance to Fusarium graminearum.

Fusarium head blight, caused primarily by Fusarium graminearum (Fg), is one of the most devastating diseases of wheat. Host resistance in wheat is classified into five types (Type-I to Type-V), and a majority of moderately resistant genotypes carry Type-II resistance (resistance to pathogen spread in the rachis) alleles, mainly from the Chinese cultivar Sumai 3. Histopathological studies in the past failed to identify the key tissue in the spike conferring resistance to pathogen spread, and most of the studies used destructive techniques, potentially damaging the tissue(s) under study. In the present study, nondestructive synchrotron-based phase contrast X-ray imaging and computed tomography techniques were used to confirm the part of the wheat spike conferring Type-II resistance to Fg spread, thus showcasing the application of synchrotron-based techniques to image host-pathogen interactions. Seven wheat genotypes of moderate resistance to Fusarium head blight were studied for changes in the void space volume fraction and grayscale/voxel intensity following Fg inoculation. Cell-wall biopolymeric compounds were quantified using Fourier-transform midinfrared spectroscopy for all genotype-treatment combinations. The study revealed that the rachilla and rachis nodes together are structurally important in conferring Type-II resistance. The structural reinforcement was not necessarily observed from lignin deposition but rather from an unknown mechanism.

Detection of quinoa flour adulteration by means of FT-MIR spectroscopy combined with chemometric methods

Quinoa flour has been receiving an increasing attention as a substitute for wheat flour in bread formulations due to immuno-nutritional features. This growing interest in quinoa has increased the demand and consequently the prices, being a target for possible adulterations with cheaper cereals. Fourier transform Mid-infrared spectroscopy (FT-MIR) was used in the present work as a fingerprinting technique to detect the presence of three adulterants (soybean, maize and wheat flours). Partial least squares discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) models were used to classify pure from adulterated samples. 414 samples were measured, including pure quinoa flour, pure adulterant flours and adulterated quinoa flours using three different proportions (10, 5 and 1% w/w). PLS-DA showed better classification results than SIMCA, with error rates from 2 to 8% for the three strategies used to detect the presence of adulterants.

An FTIR spectroscopic study and quantification of 2-amino-2-methyl-1-propanol, piperazine and absorbed carbon dioxide in concentrated aqueous solutions

This study reports the investigation of carbon dioxide (CO2) absorption into an amine blend solution of 2-amino-2-methyl-1-propanol (AMP) and piperazine (PZ). The reaction in the liquid phase between CO2 and the amines were qualitatively and quantitatively monitored by Fourier Transform Mid-Infrared spectroscopy (mid-FTIR). A multivariate partial least square regression (PLS2) model was obtained to quantify free or non-reacted AMP and PZ and absorbed CO2 in all chemical forms, i.e. no differentiation was made into carbonates or carbamates. The calibration model was constructed using a single wide region and 270 calibration samples. The concentration of AMP, PZ and CO2 from 568 samples were simultaneously predicted with low relative errors.

Detection of adulterants in grape nectars by attenuated total reflectance Fourier-transform mid-infrared spectroscopy and multivariate classification strategies

There is no any doubt about the importance of food fraud control, as it has implications in food safety and in consumer health. Focusing on fruit beverages, some types of adulterations have been detected more frequently, such as substitution with less expensive fruits. A methodology based on attenuated total reflectance Fourier-transform mid-infrared spectroscopy (ATR-FTIR) and multivariate classification was applied to detect whether grape nectars were adulterated by substitution with apple juice or cashew juice. A total of 126 samples were obtained and analyzed. Two strategies were proposed: one-class and multiclass approaches. Soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and partial least squares density modeling (PLS-DM) were used to build the models. Among them, PLS-DA presented the best performance with a sensitivity and specificity of nearly 100%. The multiclass strategy was preferred if the adulterants to be studied are known because it provides additional information.

Development of Fourier-transformed mid-infrared spectroscopy prediction models for major constituents of fractions of delactosated, defatted milk obtained through ultra- and nanofiltration

Milk filtration procedures are gaining relevance in the dairy industry because milk ultra- and nanofiltrates are used to increase milk processing efficiency, and as additives for products with improved nutraceutical properties. This study aimed to develop Fourier-transformed mid-infrared spectroscopy calibrations for ultra- and nanopermeate and retentate fractions of defatted and delactosated milk. A total of 154 samples from different milk fractions were collected and analyzed using reference methods to determine protein, solids-not-fat, glucose, and galactose content. The obtained values were matched with their respective Fourier-transformed mid-infrared spectroscopy spectra to develop new prediction models. Calibrations for each trait were built following 3 different approaches to get the best prediction models: (1) using the entire data set, (2) using 3 subsets based on component concentrations (level approach), and (3) using hierarchical clusters calculated with pairwise Mahalanobis distance among spectra (cluster approach). Calibrations were developed using partial least squares regression, after removing low signal-to-noise ratio wavelengths, and validated through a leave-one-out cross-validation procedure. In addition, the accuracy of the predicted values within each fraction was checked for each approach. Dividing the data set into subsets improved prediction models for each trait and for the samples in each milk fraction. Without considering milk fraction, the best improvement was observed for glucose and galactose. Glucose ratio performance deviation in cross-validation (RPD) increased from 7.42 to 11.31 and 11.06, for cluster and level approaches, respectively, whereas galactose RPD increased from 8.86 to 11.69 and 11.27 for cluster and level approaches, respectively. Considering milk fractions, the best improvement was observed for protein content, where RPD ranged from 0.08 to 6.06 for the whole data set calibration, whereas it ranged from 0.43 to 40.34 for the subset calibration approaches. Cluster and level approaches to build calibration models were comparable for samples from different fractions, suggesting that the 2 subsetting protocols should be both investigated to get the best prediction performances.

Structural study of humin and its interaction with humic acids by Fourier-transform mid-infrared spectroscopy.

Structural study of humin and its interaction with humic acids by Fourier-transform mid-infrared spectroscopy.

Synergistic strategy for the geographical traceability of wild Boletus tomentipes by means of data fusion analysis

Due to the worldwide importance and public interest concerning to the food quality and safety, there is a growing trend in the geographical trace of food products with various techniques. The purpose of the present study is to evaluate integrated information as an effective strategy for the geographical traceability of wild Boletus tomentipes. To this goal, 76 fruiting bodies were collected, and two mushroom parts (pileus and stipe) were analyzed by Fourier transform-mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES). Five related data matrices (FT-MIR_P, FT-MIR_S, ELE_P, ELE_S and ELE_QC/S) were fused at low- and mid-level, support vector machine (SVM) and random forest (RF) classification algorithms combined with data matrices were used for authenticating the geographical origin. The results of multivariate statistical analysis indicate that data fusion, taking advantage of the information synergy, shows the better classification performance than individual decision making. Also, the comparison of categorized result between two fusion levels suggests that, mid-level data fusion is more stable than low-level among different type models, which could be used as a reliable method for the geographical authentication purposes of wild porcini mushrooms.

Coupling pyrolysis with mid-infrared spectroscopy (Py-MIRS) to fingerprint soil organic matter bulk chemistry

A novel method coupling pyrolysis with mid-infrared spectroscopy (Py-MIRS) was developed to characterize soil organic matter (SOM) chemistry in soils. The pyrolyzer was interfaced to the MIR spectrometer by means of a Brill cell™ (CDS Analytica). The set-up generates pyrolysis fingerprint spectra from which individual pyrolysis products can be related to SOM bulk chemistry. Py-MIRS development involved the testing of experimental conditions like pyrolysis temperature (550, 700, 1000 °C), heating rate (20 °C s−1 and 20 °C ms−1) and time (15, 30 and 60 s) using reference standard compounds ranging from carbohydrates to phenols varying in chemical and structural composition like levoglusogan, gluten, tannin, syringol, pectin and leucine falling within different compound categories (carbohydrates, amino acids, proteins, phenols, etc.) as well as soil samples. Pyrolysis yields of prominent specific functional groups, like aliphatics (CH stretching at 2930 cm−1) and CC aromatics (1510 cm−1), varied with pyrolysis temperature, heating rate and time. The preferred settings for high pyrolysis yield and minimized secondary reactions were obtained at a pyrolysis temperature of 700 °C, heating rate of 20 °C ms−1 and heating time of 30 s. The suitability of Py-MIRS to detect changes in SOM composition was evaluated by comparing Py-MIRS results to Diffuse Reflectance Fourier Transform mid-Infrared Spectroscopy (DRIFTS) results. Soil samples taken from the Static Fertilization Experiment, Bad Lauchstädt, Germany (Chernozem) revealed a major SOM contribution of the peak at 1750 cm−1 (CO), followed by peaks at 950 (CH), 1510 (CC), 1176 (CH, OH) cm−1, with smaller contributions from the 2930 (CH) and 3015 (CH4) cm−1 peaks, apart from a dominant CO2 peak. Using the preferred pyrolysis settings, Py-MIRS as well as DRIFTS results further indicated that soils receiving organic (e.g. farmyard manure) inputs were highly enriched in aliphatic groups, while their absence favored the accumulation of carboxyl and aromatic groups as well as polysaccharides. Py-MIRS allowed via semi-quantification of pyrolysis products a rapid monitoring of SOM bulk chemistry with a high degree of reproducibility. It was concluded that Py-MIRS represents a fast, effective and reproducible technique to characterize changes in the SOM bulk chemistry as a result of management practices. It also allows to minimize acknowledged constraints of other analytical techniques used to characterize SOM bulk chemistry such as mineral interferences and associated secondary reactions.

Physico-chemical description of titanium dioxide–cellulose nanocomposite formation by microwave radiation with high thermal stability

Titanium dioxide–cellulose nanocomposites were nucleated and grown by decomposition of titanium isopropoxide in ethanol media together with wood cellulose fibers into the microwave-assisted solvothermal (MAS) system; the low temperature and fast formation time of the nanocomposites stand out in this methodology. The MAS method was successfully applied in the synthesis of TiO2 with anatase structure in wood cellulose fibers to produce TiO2–cellulose nanocomposites based on hydrolysis, condensation and subsequent polymerization of titanium nanoparticles on cellulose fibers. Through scanning electron microscope it was possible to confirm nanocomposite formation by impregnation/nucleation/growth of titanium dioxide (TiO2) nanoparticles on the fiber walls. The nanocomposites X-ray powder diffraction showed peaks of crystalline titanium dioxide anatase phase associated to additional cellulose diffraction remarks, as well as the absence of the CaCO3 phase, proving the nanocomposite design concept. This means that the inclusion of TiO2 nanoparticles on fibers does not alter the crystalline structure of cellulose, also confirmed by Fourier-transform mid-infrared spectroscopy. Based on the thermogravimetric analysis, nanocomposites are thermally more stable than pure cellulose, reaching a 19% difference of mass loss reduction.

Determination of Iron in Vegetable Oil by Fourier Transform Mid-Infrared Spectroscopy

The potential of Fourier transform mid-infrared spectroscopy with attenuated total reflection (FTIR-ATR) for the quantification of iron (Fe) in vegetable oil extracted from the fruit of the moriche palm (buriti) [Mauritia flexuosa] was evaluated. This green method enables direct measurements without previous sample handling. Twenty-five buriti samples were collected in Roraima (Brazil). The statistical models were developed using the technique of partial least squares (PLS) analysis and the data set was divided into two parts: one used for calibration (n = 20) and one used for testing (n = 5). First, the model was calibrated and cross-validated with the calibration data set so that the model was validated with the test data set to verify its prediction ability. To obtain reference data, the samples were analyzed by X-ray fluorescence (EDXRF). The coefficient of determination (R2) was 0.9965 and the mean square error of prediction (RMSEP) obtained for iron (Fe) was 0.8067 (in ppm). The results showed that the prediction ability can be considered good for large quantification of iron intervals in vegetable oil, and the mean relative errors were less than ±7%. This indicated that the green method for the determination of iron (Fe) in vegetable oil by Fourier transform mid-infrared spectroscopy with attenuated total reflection can be used as an alternative method to the classic methods of analysis, because it does not use reagents harmful to the environment or operator, does not generate harmful waste, uses a fast technique, and there is minimal manipulation of the sample.

Application of Fourier-transform mid infrared spectroscopy for the monitoring of pound cakes quality during storage

The aim of the present study was to investigate the ability of MIR and texture analyzer to evaluate the quality of pound cake samples produced with palm oil and rapeseed oil throughout storage. The MIR spectra analyzed by using principal component analysis (PCA) showed a clear separation of pound cakes as a function of the storage time and the nature of the used oil in the recipe. By applying partial least square regression (PLSR), excellent prediction was obtained for hardness (R2 = 0.91; RPD = 2.26), while an approximate qualitative prediction was found for springiness (R2 = 0.73; RPD = 2.07), cohesiveness (R2 = 0.67; RPD = 1.31) and resilience (R2 = 0.65; RPD = 1.24). It could be concluded that the MIR spectroscopy could be used as a rapid and non-destructive technique for monitoring texture of pound cakes throughout storage as well as for the prediction of their hardness.

Feature Fusion of ICP-AES, UV-Vis and FT-MIR for Origin Traceability of Boletus edulis Mushrooms in Combination with Chemometrics.

Origin traceability is an important step to control the nutritional and pharmacological quality of food products. Boletus edulis mushroom is a well-known food resource in the world. Its nutritional and medicinal properties are drastically varied depending on geographical origins. In this study, three sensor systems (inductively coupled plasma atomic emission spectrophotometer (ICP-AES), ultraviolet-visible (UV-Vis) and Fourier transform mid-infrared spectroscopy (FT-MIR)) were applied for the origin traceability of 184 mushroom samples (caps and stipes) in combination with chemometrics. The difference between cap and stipe was clearly illustrated based on a single sensor technique, respectively. Feature variables from three instruments were used for origin traceability. Two supervised classification methods, partial least square discriminant analysis (FLS-DA) and grid search support vector machine (GS-SVM), were applied to develop mathematical models. Two steps (internal cross-validation and external prediction for unknown samples) were used to evaluate the performance of a classification model. The result is satisfactory with high accuracies ranging from 90.625% to 100%. These models also have an excellent generalization ability with the optimal parameters. Based on the combination of three sensory systems, our study provides a multi-sensory and comprehensive origin traceability of B. edulis mushrooms.

Comparative analysis of different transformed Saccharomyces cerevisiae strains based on high-throughput Fourier transform infrared spectroscopy

This study shows the application of the Fourier transform mid-infrared spectroscopy (FT-MIR) associated with high-throughput technology to study the biochemical fingerprints of different Saccharomyces cerevisiae strains transformed with the same expression system along the similar cultivation in bioreactor. The phenotype, as well as the cellular metabolism and recombinant cyprosin biosynthesis, were determined. The differences observed were confirmed by conventional cyprosin activity protocol, and the metabolic evolution was analyzed using high-performance liquid chromatography technique. The spectral analysis based on chemometrics tools, such as the principal component analysis, is a useful methodology for the phenotypes characterization as well as the specific metabolic states along the cultivations according to the clusters created. The ratio bands of spectra also represented a useful tool to evaluate the metabolic and biochemical differences between both expression systems, allowing to have an additional parameter to the biomolecular comparison. Therefore, high-throughput FT-MIR spectroscopy associated with multivariate data analysis represent a valuable strategy for extracting significant specific biomolecular information along the cultivation, providing a complete bioprocess analysis, once it detects slight molecular changes which it will be useful for screening and optimization process in the biotechnological or pharmaceutical industry.