Fourier Transform Mid-infrared Research Articles

Use of genetic algorithms in the wavelength selection of FT-MIR spectra to classify unifloral honeys from Sardinia

Beekeeping is among the oldest activities in Sardinia (Italy). Among others, here are produced four valuable unifloral honeys appreciated worldwide for their quality and organoleptic properties, i.e., asphodel (Asphodelus microcarpus), eucalyptus (Eucalyptus camaldulensis), strawberry tree (Arbutus unedo L.) and thistle (Galactites tomentosa).The main purpose of this contribution was to assess a botanical classification method by analyzing 125 honeys using Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy.Spectra were used to develop a predictive model by means of linear discriminant analysis (LDA), using different spectral pretreatments techniques. Predictors were selected using principal component analysis (PCA) or genetic algorithms (GA) tools. In particular, GA selected 34 wavelengths in the spectral regions from 1726 to 1543 cm−1, and the application of LDA to this selection provided an accuracy of 93.6% in cross validation and an accuracy of 87.8% in the validation on a test set of honey samples.The results were compared, in terms of pros and cons, with other targeted and non-targeted approaches previously assessed by this research group on the same four unifloral honeys.

Comparison of the genetic characteristics of directly measured and Fourier-transform mid-infrared-predicted bovine milk fatty acids and proteins.

Fourier-transform mid-infrared (FT-MIR) spectroscopy is a high-throughput and inexpensive methodology used to evaluate concentrations of fat and protein in dairy cattle milk samples. The objective of this study was to compare the genetic characteristics of FT-MIR predicted fatty acids and individual milk proteins with those that had been measured directly using gas and liquid chromatography methods. The data used in this study was based on 2,005 milk samples collected from 706 Holstein-Friesian × Jersey animals that were managed in a seasonal, pasture-based dairy system, with milk samples collected across 2 consecutive seasons. Concentrations of fatty acids and protein fractions in milk samples were directly determined by gas chromatography and high-performance liquid chromatography, respectively. Models to predict each directly measured trait based on FT-MIR spectra were developed using partial least squares regression, with spectra from a random selection of half the cows used to train the models, and predictions for the remaining cows used as validation. Variance parameters for each trait and genetic correlations for each pair of measured/predicted traits were estimated from pedigree-based bivariate models using REML procedures. A genome-wide association study was undertaken using imputed whole-genome sequence, and quantitative trait loci (QTL) from directly measured traits were compared with QTL from the corresponding FT-MIR predicted traits. Cross-validation prediction accuracies based on partial least squares for individual and grouped fatty acids ranged from 0.18 to 0.65. Trait prediction accuracies in cross-validation for protein fractions were 0.53, 0.19, and 0.48 for α-casein, β-casein, and κ-casein, 0.31 for α-lactalbumin, 0.68 for β-lactoglobulin, and 0.36 for lactoferrin. Heritability estimates for directly measured traits ranged from 0.07 to 0.55 for fatty acids; and from 0.14 to 0.63 for individual milk proteins. For FT-MIR predicted traits, heritability estimates were mostly higher than for the corresponding measured traits, ranging from 0.14 to 0.46 for fatty acids, and from 0.30 to 0.70 for individual proteins. Genetic correlations between directly measured and FT-MIR predicted protein fractions were consistently above 0.75, with the exceptions of C18:0 and C18:3 cis-3, which had genetic correlations of 0.72 and 0.74, respectively. The GWAS identified trait QTL for fatty acids with likely candidates in the DGAT1, CCDC57, SCD, and GPAT4 genes. Notably, QTL for SCD were largely absent in the FT-MIR predicted traits, and QTL for GPAT4 were absent in directly measured traits. Similarly, for directly measured individual proteins, we identified QTL with likely candidates in the CSN1S1, CSN3, PAEP, and LTF genes, but the QTL for CSN3 and LTF were absent in the FT-MIR predicted traits. Our study indicates that genetic correlations between directly measured and FT-MIR predicted fatty acid and protein fractions are typically high, but that phenotypic variation in these traits may be underpinned by differing genetic architecture.

Quality control strategies of medicine food homology materials based on fingerprint profiling and chemometrics: Citri Reticulata Pericarpium as an example

The study aimed to provide a reliable and feasible strategy for the comprehensive quality control of medicine food homology materials (MFHM). The high performance liquid chromatography (HPLC) fingerprints and Fourier transform mid-infrared (FT-MIR) quantized fingerprints were successfully developed to comprehensively evaluate overall quality of Citri Reticulata Pericarpium (CRP) by applying comprehensive quantified fingerprint method (CQFM). All samples were well distinguished and divided into 5 grades. In addition, through principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), the identification ability of HPLC fingerprints and FT-MIR fingerprints on CRP with different storage years was discussed. The results showed that HPLC fingerprints combined with PCA had good discrimination ability, and the PLS-DA model established by the preprocessed FT-MIR fingerprint data could accurately distinguish and predict the storage period of CRP. Finally, based on 1, 1-diphenyl-2-picrylhydrazyl radical (DPPH•) scavenging assay, combined with bivariate correlation analysis, the fingerprint-activity relationship of offline antioxidant activity of CRP samples with the fingerprints peak were studied. In general, the comprehensive strategies provide a reliable and scientific reference scheme for the quality control of MFHM in the future.

Annual and seasonal trends in cow's milk quality determined by FT-MIR spectroscopy in Hungary between 2011 and 2020.

We analysed and monitored the major chemical composition of cow's bulk milk by Fourier transform mid-infrared (FT-MIR) spectroscopy over a 10-year period in the whole territory of Hungary. In addition, the two most important key parameters for milk quality assessment, total bacterial count (TBC) and somatic cell count (SCC) were also followed. Production parameters showed significant seasonal and yearly changes. The overall mean fat, protein, lactose and solids-non-fat (SNF) contents of cow's milk were 3.81%, 3.32%, 4.74% and 8.76%, respectively. A circannual variation was observed in the chemical composition and yield of milk components of samples examined between 2011 and 2020. Concerning milk fat, milk protein and SNF, the values were the lowest in summer and the highest in winter. In the case of lactose, the minimum values were measured in autumn and the maximum values in spring. An obvious trend of long-term elevation of lactose and SNF was found in the raw cow milk samples over the observed period. The overall mean TBC and SCC of cow's milk were 52 × 103 CFU ml-1 and 270 × 103 cells/ml, respectively. Although there were differences in the monthly average values, no seasonal cyclicality was observed.

PSVIII-19 Predict Indigestible Fiber Fraction of Barley Plant Silage by Using non-Destructive Mid-IR vs Near-IR Spectroscopic Techniques

Abstract The objective of this study was to reveal the potential of using Fourier transform mid-infrared (FTIR) and near infrared (NIR) spectroscopy as tools for the determination of indigestible neutral (NDF) fraction (iNDF) of barley plant silage. A total of 48 barley plant silage samples collected from different farms in Western Canada provinces were analyzed for iNDF. Reference values were matched with NIR and FTIR spectra. Spectral data processing (pretreatments) included first derivative (FD), standard normal variate (SNV), multiplicative scattering correction (MSC), second derivative (SD) and orthogonal signal correction (OSC). Prediction equations were obtained from each model using an external validation set. The determination coefficient of external validation (R2P) of iNDF was 0.62 for FTIR, while 0.41 for NIR and the corresponding ratio performance deviation (RPD) were 1.69 and 1.38 in FTIR and NIR, respectively. Results from this research showed the high potential of applying infrared molecular spectroscopy for the examination of forage plant fiber digestibility. More studies are needed to improve the accuracy and performance of FTIR and NIR spectroscopies in predicting the iNDF of barley plant silage samples.

Evaluation of prediction of indigestible fiber fraction (iNDF) of whole-crop barley silage by using non-destructive spectroscopic techniques as a fast-screening method: comparison between FTIR vs. NIR

The objective of this study was to reveal the potential of using Fourier transform mid-infrared (FTIR) and near infrared (NIR) spectroscopy as tools for the determination of indigestible neutral (NDF) fraction (iNDF) of whole-crop barley silage. A total of 48 whole-crop barley silage samples collected from 48 different farms in Western Canada were analyzed for iNDF. Reference values were matched with NIR and FTIR spectra. Spectral data processing (pretreatments) included first derivative, standard normal variate, multiplicative scattering correction, second derivative, and orthogonal signal correction. Prediction equations were obtained from each model using an external validation set. The coefficient of determination for the external validation of iNDF was 0.62 for FTIR and 0.41 for NIR, while the corresponding ratio performance deviation was 1.69 and 1.38 for FTIR and NIR, respectively. Results from this research showed the high potential of applying infrared molecular spectroscopy for the examination of forage plant fiber digestibility. More studies are needed to improve the accuracy and performance of FTIR and NIR spectroscopies in predicting the iNDF of whole-crop barley silage samples.

Can unsupervised learning methods applied to milk recording big data provide new insights into dairy cow health?

Among the dairy sector's current concerns, the assessment of global animal health status is a complex challenge. Its multidimensionality means that global monitoring tools are rarely considered. Instead, specific disease detection is often studied separately and, due to financial and ethical issues, uses small-scale data sets focusing on few biomarkers. Several studies have already been conducted using milk Fourier transform mid-infrared (FT-MIR) spectroscopy to detect mastitis and lameness or to quantify health-related biomarkers in milk or blood. Those studies are relevant but they focus mainly on one biomarker or disease. To solve this issue and the small-scale data set, in this study, we proposed a holistic approach using big data obtained from milk recording, including milk yield, somatic cell count, and 27 FT-MIR-based predictors related to milk composition and animal health status. Using 740,454 records collected from 114,536 first-parity Holstein cows in southern Belgium, we performed repeated unsupervised learning algorithms based on Ward's agglomerative hierarchical clustering method to find potential interesting patterns. A divide-and-conquer approach was used to overcome the limitation of computational resources in clustering a relatively large data set. Five groups of records were identified. Differences observed in the fourth group suggested a relationship to metabolic disorders. The fifth group seemed to be related to mastitis. In a second step, we performed a partial least squares discriminant analysis (PLS-DA) to predict the probability of belonging to those specific groups for the entire data set. The obtained global accuracy was 0.77 and the balanced accuracy (i.e., the mean between sensitivity and specificity) of discriminating the fourth and fifth groups was 0.88 and 0.96, respectively. Then, a validation of the interpretation of those groups was performed using 204 milk and blood reference records. The predicted probability associated with the metabolic disorders issue had significant correlations of 0.54 with blood β-hydroxybutyrate, 0.44 with blood nonesterified fatty acids, -0.32 with blood glucose, -0.23 with milk glucose-6-phosphate, and 0.38 with milk isocitrate. In contrast, the predicted probability of belonging to the mastitis group had correlations of 0.69 with milk lactate dehydrogenase, 0.46 with milk N-acetyl-β-d-glucosaminidase, -0.18 with milk free glucose, and 0.16 with milk glucose-6-phosphate. Consequently, these results suggest that the obtained quantitative traits indirectly reflect some of the main health disorders in dairy farming and could be used to monitor dairy cows on a large scale. By using unsupervised learning on large-scale milk recording data and then validating the pattern using reference laboratory measures, we propose a new approach to quickly assess dairy cow health status.

Diurnal variation of milk fatty acids in early-lactation Holstein cows with and without hyperketonemia

Estimates of milk constituents by Fourier-transform mid-infrared (FTIR) analysis have been shown to be a useful tool in monitoring energy deficit in early-lactation dairy cows. Our objectives were to describe the diurnal variation in milk fatty acids (FAs) and estimate the association of hyperketonemia with concentrations and diurnal patterns of FTIR estimates of milk FA. Blood samples were collected via jugular catheters bihourly for 5 d from multiparous Holstein cows (n = 28) enrolled between 3 and 9 days in milk. Milk samples were collected thrice daily at 0600, 1400, and 2200 h for d 2, 3, and 4 of the study period. Cows were retrospectively classified as hyperketonemic (HYK; n = 13) or non-HYK (n = 15) based on blood beta-hydroxybutyrate (bBHB) concentrations analyzed during the study period. Cows were classified as HYK if bBHB was ≥ 1.2 mmol/l for ≥ 50% (22/44) of bihourly timepoints; cows were classified as non-HYK if bBHB was ≥ 1.2 mmol/l for < 50% of bihourly timepoints. The HYK cows had bBHB ≥ 1.2 mmol/l for 31.4 ± 6.8 timepoints while the non-HYK cows had bBHB ≥ 1.2 mmol/l for 8.0 ± 3.9 timepoints. We used generalized linear mixed models to analyze concentrations of milk FA over time and differences between HYK groups. The relative percentage of de novo, mixed, and preformed FAs all followed diurnal patterns, however only the yield of preformed FA diurnally cycled, reaching a nadir at 0600 h and peaking at 1400 h. The yield per milking of preformed FA was also greater in the HYK cows than in the non-HYK cows. Oleic acid in milk followed a similar diurnal pattern to the yield of preformed FA, likely driving the cyclical nature of preformed FA. Finally, stearic acid was greater in HYK cows. Our results suggest that FTIR estimates of milk FA offer the potential to provide insight on the energy status of early-lactation cows, and when interested in understanding the absolute concentrations and yields of milk FA, diurnal variation should be considered.

Production, Composition, and Ecological Function of Sweet-Basil-Seed Mucilage during Hydration

The sweet-basil (Ocimum basilicum L.) fruit/pericarp produces mucilage that engulfs the fruit and seed within minutes of hydration. Seed mucilage is produced by plant species that have adapted to arid, sandy soils. This study was conducted to determine how basil-seed mucilage improves ecological fitness. A second objective was to find ways to remove mucilage, which may interfere with commercial planting. Basil fruit/seeds were examined using light and environmental scanning electron microscopy. Columnar structures of basil mucilage rapidly unfolded from the pericarp upon initial hydration. Dilute hydrochloric acid removed the mucilage, which decreased the water content four-fold but did not inhibit seed germination in a laboratory test. Nondestructive Fourier-transform mid-infrared (FTIR) spectroscopy confirmed that the mucilage was primarily composed of hemicellulose that anchored the basil seed to resist movement. The fully hydrated seeds approached zero water potential, so the mucilage did not interfere with hydration. The seeds that were planted in growing media with mucilage had from 12 to 28% higher seedling emergence and survival percentages after 10 days than seeds without mucilage. Basil-fruit/seed mucilage provides a reservoir of loosely bound water at high water potential for seed germination and early seedling development, thus improving survivability under low moisture.

Information theory and machine learning based authentication of flaxseed oil using portable and handheld vibrational spectroscopy sensors

Flaxseed oil is one of the popular edible oils, and its adulteration has become a concern because of its high commodity value. Therefore, there is a need to detect adulterated flaxseeds with a rapid, non-invasive, and simple method to keep the quality of flaxseed oil. This study aimed to develop a machine learning algorithm and information theory to detect adulterated flaxseed oils by vibrational spectroscopy units combined with chemometrics. Oil spectra were collected with portable/handheld vibrational spectroscopic units and analyzed using conditional entropy, support vector machines (SVM), and soft independent modeling of class analogy (SIMCA) to generate classification models to identify the pureness of oils and partial least square regression (PLSR) to predict the adulterant levels and fatty acid distributions. Conditional entropy showed that two or three wavenumbers/shifts could discriminate the oils using machine learning algorithms with an accuracy of over 95%. SIMCA models determined the oils with an interclass distance (ICD) over 1.1. Specifically, the Fourier transform mid-infrared (FT-MIR) unit provided the best classification of the oils with ICD over 4.9. All instruments showed good performance in predicting fatty acids and adulteration levels with r val ≥0.89 and standard error prediction (SEP) ≤1.7%. Overall, these portable/handheld units showed great potential for rapid, simple, and non-invasive monitoring to identify adulterated flaxseed oils.

Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices.

Nowadays, coffee, cocoa, and spices have broad applications in the food and pharmaceutical industries due to their organoleptic and nutraceutical properties, which have turned them into products of great commercial demand. Consequently, these products are susceptible to fraud and adulteration, especially those sold at high prices, such as saffron, vanilla, and turmeric. This situation represents a major problem for industries and consumers’ health. Implementing analytical techniques, i.e., Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis, can ensure the authenticity and quality of these products since these provide unique information on food matrices. The present review addresses FT-MIR spectroscopy and multivariate analysis application on coffee, cocoa, and spices authentication and quality control, revealing their potential use and elucidating areas of opportunity for future research.

The use of milk Fourier-transform mid-infrared spectroscopy to diagnose pregnancy and determine spectral regional associations with pregnancy in US dairy cows

Accurate early diagnosis of pregnancy is important for timely reproductive management of dairy farms. Fourier-transform mid-infrared (FT-MIR) milk spectral data are routinely used for determining milk components such as fat and protein, whereas milk composition is known to change with advancing stages of pregnancy. The objectives of this study were to compare partial least squares discriminant analysis (PLS-DA) and a Bayesian variable selection regression model (BayesC) for the diagnosis of pregnancy status (PS) from milk FT-MIR data and to infer any spectral regions that might be highly associated with PS at various stages of pregnancy. Conception dates on confirmed pregnant cows were obtained from Holstein cows within 123 herds in Michigan, Ohio, and Indiana during 2018 and 2019. Milk samples from these pregnant cows at 7 different stages of pregnancy were case-control matched to open contemporary herd mates to be within the same stage (±10 d for days in milk) of lactation for the same milk sample test date. The FT-MIR data were obtained for all of these milk samples. Ten-fold herd-independent cross-validation was used to compare PLS-DA versus BayesC using the area under the receiver operating characteristic curve (AUC). The BayesC model demonstrated higher mean AUC compared with PLS-DA at all stages exceeding 60 d of pregnancy. The mean BayesC AUC at stage 1 (1-30 d) was 0.58 ± 0.02, which was superior to a random guess (AUC = 0.50) yet too low to be of practical use. The mean BayesC AUC at stage 7 (≥180 d) was 0.13 greater compared with that of stage 1 (1-30 d) and 0.07 to 0.10 greater compared with stages 2, 3, 4, 5, and 6 (31-180 d in 30-d increments). The mean AUC of stages 2 to 6 were 0.03 to 0.06 greater compared with stage 1 yet again too low to be of practical use. Because of high multicollinearity between many adjacent wavenumbers, a spatially constrained clustering algorithm was used to adaptively partition wavenumbers into 68 windows before inferring associations of spectral regions with pregnancy. Pregnancy status was highly associated with wavenumber windows 1,063 to 1,134 cm-1, 1,201 to 1,257 cm-1, and 1,260 to 1,432 cm-1 based on an estimated BayesC posterior probability of association (PPA) approaching 100% for each of these windows at all pregnancy stages. Other windows ranging from 1,730 to 1,764 cm-1, 1,775 to 1,992 cm-1, 1,995 to 2,163 cm-1, and 2,167 to 2,316 cm-1 had varying medium to high PPA (30% to 100%) across stages. The estimated PPA in wavenumber regions from 1,477 to 1,507 cm-1, and 1,510 to 1,574 cm-1 was weaker in stages 1 and 2 compared with later stages, whereas for the regions 2,984 to 3,077 cm-1 and 3,081 to 3,133 cm-1 the effect of pregnancy was greater for stage 1 compared with other stages. Despite our conclusion that milk FT-MIR data poorly diagnose PS, our study provides new insights into spectral regions that are strongly associated with PS and warrant greater attention.

Origin geographical classification of green coffee beans (Coffeaarabica L.) produced in different regions of the Minas Gerais state by FT-MIR and chemometric

The present work was proposal the potential evaluation of Fourier-Transform Mid-Infrared (FT-MIR) associated with chemometric approach in green beans, in order to discriminate the origin of special Arabica coffees in a single state that has heterogeneous environments. Partial Least Squares Discriminant Analysis (PLS-DA) model presented as result: 3 latent variables, R2X (cum) = 0.892, R2Y (cum) = 0.659; Q2Y (cum) = 0.494, RMSEP = 0.182387, p-value CV-Anova = 0.009, 100% of both sensitivity and specificity and the prediction classification obtained was: 100, 83.33, 100, 83.33% for class 1, class 2, class 3 and class 4, respectively. These results can be considered adequate for the proposed hypothesis. The obtained results that the regions have markers such as trigonelline, chlorogenic and fatty acids, sensitive to absorption in the mid-infrared and that are able to determine the origin of green coffee beans of Arabica. Thus, the FT-MIR associated with chemometrics has the potential to employ speed, modernity and cost reduction in the certification of origin of coffees.

The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy

Boletes are favored by consumers because of their delicious taste and high nutritional value. However, as the storage period increases, their fruiting bodies will grow microorganisms and produce substances harmful to the human body. Therefore, we need to identify the storage period of boletes to ensure their quality. In this article, two-dimensional correlation spectroscopy (2DCOS) images are directly used for deep learning modeling, and the complex spectral data analysis process is transformed into a simple digital image processing problem. We collected 2,018 samples of boletes. After laboratory cleaning, drying, grinding, and tablet compression, their Fourier transform mid-infrared (FT-MIR) spectroscopy data were obtained. Then, we acquired 18,162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS, and integrative 2DCOS (i2DCOS) spectra of 1,750–400, 1,450–1,000, and 1,150–1,000 cm–1 bands. For these data sets, we established nine deep residual convolutional neural network (ResNet) models to identify the storage period of boletes. The result shows that the accuracy with the train set, test set, and external validation set of the synchronous 2DCOS model on the 1,750–400-cm–1 band is 100%, and the loss value is close to zero, so this model is the best. The synchronous 2DCOS model on the 1,150–1,000-cm–1 band comes next, and these two models have high accuracy and generalization ability which can be used to identify the storage period of boletes. The results have certain practical application value and provide a scientific basis for the quality control and market management of bolete mushrooms. In conclusion, our method is novel and extends the application of deep learning in the food field. At the same time, it can be applied to other fields such as agriculture and herbal medicine.

Low-pressure plasma modification of the rheological properties of tapioca starch

Starch is an important biopolymer widely used in the pharmaceutical, food and textile industries. In this study, low-pressure (0.5 atm) cold plasma was used as an novel technique to modify the rheological behaviours of native tapioca starch using four different feeding gases (air, carbon dioxide, argon, and helium). The induced changes in starch samples were studied by rapid visco analyser (RVA), Rheometer, Fourier transform mid-infrared (FTIR) spectroscopy and scanning electron microscope (SEM) images, respectively. The plasma significantly (P < 0.05) increased peak viscosity, trough viscosity and breakdown viscosity of the treated starch compared to the control. FTIR spectral absorption peaks of the different plasma treated samples were observed at 924, 994, 1149, 1335 and 3274 cm−1, which are related to C–O–C asymmetric stretching, C–OH bending, C–O–C asymmetric stretching, C–H bending and O–H stretching bonds, respectively. The differences in absorbance intensity at these frequencies revealed chemical changes in the corresponding functional groups. The results of FTIR spectral data support the theory that cross-linking reactions occur between starch molecules during plasma treatment, resulting in changes of pasting and rheological properties. However, there was no obvious fissures or cavities observed on the surface of starch granules observed using SEM imaging.

Extended application of deep learning combined with 2DCOS: Study on origin identification in the medicinal plant of Paris polyphylla var. yunnanensis.

Medicinal plants are very important to human health, and ensuring their quality and rapid evaluation are the current research concerns. Deep learning has a strong ability in recognition. This study extended it to the identification of medicinal plants from the perspective of spectrum. In order to realise the rapid identification and provide a reference for the selection of high-quality resources of medicinal plants, a combination of deep learning and two-dimensional correlation spectroscopy (2DCOS) was proposed. For the first time, Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR) spectroscopy 2DCOS images combined with residual neural network (ResNet) was used for the origin identification of Paris polyphylla var. yunnanensis. In total 1593 samples were collected and 12821 2DCOS images were drawn. The climate of different origins was briefly analysed. The xishuangbanna, puer, lincang, honghe and wenshan are the five regions with more ecological advantages. The synchronous 2DCOS models of FT-MIR and NIR could realise origin identification with the accuracy of 100%. The synchronous images were suitable for the identification of medicinal plants with complex systems. The full band, feature band and different contour models had no big difference in distinguishing ability, so they were not the key factors affecting the discrimination results. The ResNet models established were stable, reliable, and robust, which not only solved the problem of origin identification, expanded the application field of deep learning, but also provided practical reference for the related research of other medicinal plants.

Rapid Assessment of Anthocyanins Content of Onion Waste through Visible-Near-Short-Wave and Mid-Infrared Spectroscopy Combined with Machine Learning Techniques

A sustainable process for valorization of onion waste would need to entail preliminary sorting out of exhausted or suboptimal material as part of decision-making. In the present study, an approach for monitoring red onion skin (OS) phenolic composition was investigated through Visible Near-Short-Wave infrared (VNIR-SWIR) (350–2500 nm) and Fourier-Transform-Mid-Infrared (FT-MIR) (4000–600 cm−1) spectral analyses and Machine-Learning (ML) methods. Our stepwise approach consisted of: (i) chemical analyses to obtain reference values for Total Phenolic Content (TPC) and Total Monomeric Anthocyanin Content (TAC); (ii) spectroscopic analysis and creation of OS spectral libraries; (iii) generation of calibration and validation datasets; (iv) spectral exploratory analysis and regression modeling via several ML algorithms; and (v) model performance evaluation. Among all, the k-nearest neighbors model from 1st derivative VNIR-SWIR spectra at 350–2500 nm resulted promising for the prediction of TAC (R2 = 0.82, RMSE = 0.52 and RPIQ = 3.56). The 2nd derivative FT-MIR spectral fingerprint among 600–900 and 1500–1600 cm−1 proved more informative about the inherent phenolic composition of OS. Overall, the diagnostic value and predictive accuracy of our spectral data support the perspective of employing non-destructive spectroscopic tools in real-time quality control of onion waste.

Predicting weathering indices in soils using FTIR spectra and random forest models

Weathering indices based on the relative proportions of different chemical elements are a useful tool to investigate the degree of weathering of soils. This characterization is missing in West Azerbaijan, northern Iran, and thus the main goals of this work were to assess the suitability of different indices to determine soil weathering, and to predict weathering indices using Fourier Transform Mid-infrared (FTIR) spectroscopy and Random Forest (RF) models. Soil major elemental oxides were determined in 12 profiles by X-ray Fluorescence (XRF), and the degree of chemical weathering was characterized using various indices: Chemical Index of Alteration (CIA), Chemical Index of Weathering (CIW), Mineralogical Index of Alteration (MIA), Weathering Index of Parker (WIP), SiO2/R2O3, Vogt Index (V) and Chemical Proxy of Alteration (CPA). For each index, a RF model based on the FTIR spectra of the samples was optimized and calibrated. The soils showed a weak to moderate degree of weathering, and CIA, CIW, MIA and V were the most appropriate for this characterization. The constructed models showed R2 values of 0.79, 0.75 and 0.71 for MIA, CIA, and CIW, respectively. The important spectral bands for prediction were those related to the presence of smectitic clays, which indicates that weathering is related to the alteration of primary minerals and the neoformation of smectites. The predicted spatial distribution of the weathering indices showed that Typic Haplocalcids and Typic Haploxerepts are at an earlier stage of weathering compared to Haplargids and Haplocambids in the area. The combination of FTIR spectroscopy and RF models is a rapid, efficient, and cost-effective technique to predict weathering indices in large datasets when XRF data are limited.

Classification of the geographical origin of argan kernels by infrared spectroscopy and chemometrics

The main objective of this study is the use of Fourier transform mid-infrared (FTIR) spectroscopy for discrimination of samples of Argan kernels from three Moroccan regions. The content of Argan oil extracted from the kernel depends on the plant genotype and on the environmental conditions. The origin of argan kernel samples could be distinguished combining the IR spectra measurement and chemometric tools for discriminant analysis. A multivariate analysis procedure based on the combined use of Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) was tested for this purpose. HCA showed three distinct clusters related to the Moroccan regions and suggested the usefulness of IR spectroscopy to distinguish among kernel origins. Afterwards, PLS-DA was used for the discrimination and classification of the origin of the various argan kernels and good classification models and validation results were obtained. For 9 new unknown samples we attribuate 100% these samples by external validation. The results demonstrated that the combined use of FTIR and chemometric analysis (cluster analysis and discrimination by PLS- DA) can be used to rapidly and simply determine the origine of argan kernels, a relevant task for the agroalimentary Moroccan industry.

Assessing the impacts of climate change and habitat suitability on the distribution and quality of medicinal plant using multiple information integration: Take Gentiana rigescens as an example

Gentiana rigescens is an economically important medicinal plant in the subtropical zone in Asia. However, it is unclear how its distribution responds to climate change in the future. This study aims to simulate the effect of climate change on the distribution of G. rigescens by using the maximum entropy (MaxEnt) model and to evaluate the potential impact of habitat suitability on the quality of medicinal materials vis multiple chemical information. In the study, we investigated the suitable habitat fluctuation ranges of G. rigescens under different future climate scenarios at first. Subsequently, the chemical geographical variation of medicinal materials collected from different suitable areas was evaluated based on high-performance liquid chromatography (HPLC) analysis and Fourier-transform mid-infrared (FT-MIR) spectroscopy. The results showed that the areas of highly suitable habitat for G. rigescens was about 23.17 × 104 km2, and more than 98.88% of highly suitable habitat was distributed in the southwest of China. The survival of G. rigescens may be threatened by global climate change in the future. Under the Representative Concentration Pathways 8.5 (RCP8.5) climate scenario, the change ratio of the highly suitable habitat will be −99.22% in 2070. Meanwhile, the centroids of the highly suitable area showed a westward-shifting trend under future emission trajectories. HPLC analysis showed that medicinal materials from highly suitable areas contained higher concentrations of total iridoid glycoside. Analysis results of FT-MIR data combined with OPLS-DA modeling indicated that the overall chemical composition of samples collected from different suitable habitats were different, and the habitat suitability had a stronger effect on the chemical composition in leaf than that in stem and root. Based on the concentration of iridoids and FT-MIR fingerprint, our research demonstrated there was a certain correlation of habitat suitability with the overall chemical variation of G. rigescens. The highly suitable habitat was better for the cultivation of high-quality medicinal materials. In summary, these findings would improve our understanding of the distribution and quality variation of medicinal Gentiana under future climate change. Meanwhile, the study approach based on multiple information integration could provide a useful case for research of other medicinal plants.