Determination of Bio-Based Fertilizer Composition Using Combined NIR and MIR Spectroscopy: A Model Averaging Approach.

Application of bio-based fertilizers is considered a practical solution to enhance soil fertility and maintain soil quality. However, the composition of bio-based fertilizers needs to be quantified before their application to the soil. Non-destructive techniques such as near-infrared (NIR) and mid-infrared (MIR) are generally used to quantify the composition of bio-based fertilizers in a speedy and cost-effective manner. However, the prediction performances of these techniques need to be quantified before deployment. With this motive, this study investigates the potential of these techniques to characterize a diverse set of bio-based fertilizers for 25 different properties including nutrients, minerals, heavy metals, pH, and EC. A partial least square model with wavelength selection is employed to estimate each property of interest. Then a model averaging, approach is tested to examine if combining model outcomes of NIR with MIR could improve the prediction performances of these sensors. In total, 17 of the 25 elements could be predicted to have a good performance status using individual spectral methods. Combining model outcomes of NIR with MIR resulted in an improvement, increasing the number of properties that could be predicted from 17 to 21. Most notably the improvement in prediction performance was observed for Cd, Cr, Zn, Al, Ca, Fe, S, Cu, Ec, and Na. It was concluded that the combined use of NIR and MIR spectral methods can be used to monitor the composition of a diverse set of bio-based fertilizers.

Cold-Pressed Pomegranate Seed Oil: Study of Punicic Acid Properties by Coupling of GC/FID and FTIR.

Over the last decades, we have witnessed an increasing interest in food-related products containing vegetable oils. These oils can be obtained either by extraction or by mechanical pressing of different parts of plants (e.g., seeds, fruit, and drupels). Producers of nutraceuticals have ceaselessly searched for unique and effective natural ingredients. The enormous success of argan oil has been followed by discoveries of other interesting vegetable oils (e.g., pomegranate oil) containing several bioactives. This work describes the pomegranate fruit extract and seed oil as a rich source of conjugated linolenic acid as a metabolite of punicic acid (PA), deriving from the omega-5 family (ω-5). Through the chemical characterization of PA, its nutritional and therapeutic properties are highlighted together with the physiological properties that encourage its use in human nutrition. We analyzed the composition of all fatty acids with beneficial properties occurring in pomegranate seed oil using gas chromatography (GC) with flame-ionization detection (FID) analysis combined with Fourier transform infrared spectroscopy (FTIR). Pomegranate seed oil mainly consists of 9,11,13-octadic-trienoic acid (18:3), corresponding to 73 wt % of the total fatty acids. Nine components were identified by GC in PSO, varying between 0.58 and 73.19 wt %. Using midinfrared (MIR) spectroscopy, we compared the composition of pomegranate seed oil with that of meadowfoam seed oil (MSO), which is also becoming increasingly popular in the food industry due to its high content of long chain fatty acids (C20-22), providing increased oil stability. From the results of FTIR and MIR spectroscopy, we found that punicic acid is unique in PSO (73.19 wt %) but not in MSO.

Home and community composts in Nantes city (France): quality and safety regarding trace metals and metalloids.

Home and community composting are key strategies for local organic waste management. The quality and safety of industrial composts are controlled, but those of home and community composts are not, and this could make them unsafe for use in kitchen gardens. Home (n = 20) and community (n = 41) composts, from urban and suburban areas including mildly Pb-contaminated allotment gardens, were analyzed for quality and safety regarding trace metals and metalloids (TMM) using mid-infrared Fourier transform spectrometry (FT-MIR) and portable X-ray fluorescence spectrometry, respectively. Home composts had a significantly higher Pb content (98 mg.kg-1 ± 10 mg.kg-1) than community composts (21 mg.kg-1 ± 2 mg.kg-1). Numerous home composts (85%) and a few community composts (17%) exceeded the organic farming thresholds for Pb (45 mg.kg-1) and Zn (100 mg.kg-1). The high mineral matter content and the relative abundance of chemical functions attributable to silicates (up to 35%) highly paralleled with TMM contents, mostly concentrated in the fine fraction. Co-inertia analysis highlighted strong and significant links between TMM contents and the whole chemical signature delivered by FT-MIR spectrometry. Pb-contaminated soil could be carried into home compost by green waste or by voluntary addition. Covariance analyses indicated that mineral matter and chemical functions only partly explained the variability in Pb content, suggesting a more complex combination of drivers. Community composting appears as a suitable local solution resulting in high-quality compost that complies with European organic farming regulations, while home composting from allotment gardens should be seriously evaluated to comply with such safety requirements.

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.

Mid-Infrared Spectroscopy as a Rapid Tool to Qualitatively Predict the Effects of Species, Regions and Roasting on the Nutritional Composition of Australian Acacia Seed Species.

In recent times, the popularity of adding value to under-utilized legumes have increased to enhance their use for human consumption. Acacia seed (AS) is an underutilized legume with over 40 edible species found in Australia. The study aimed to qualitatively characterize the chemical composition of 14 common edible AS species from 27 regions in Australia using mid-infrared (MIR) spectroscopy as a rapid tool. Raw and roasted (180 °C, 5, 7, and 9 min) AS flour were analysed using MIR spectroscopy. The wavenumbers (1045 cm-1, 1641 cm-1, and 2852-2926 cm-1) in the MIR spectra show the main components in the AS samples. Principal component analysis (PCA) of the MIR data displayed the clustering of samples according to species and roasting treatment. However, regional differences within the same AS species have less of an effect on the components, as shown in the PCA plot. Statistical analysis of absorbance at specific wavenumbers showed that roasting significantly (p < 0.05) reduced the compositions of some of the AS species. The results provided a foundation for hypothesizing the compositional similarity and/or differences among AS species before and after roasting.

Anharmonicity and Spectra-Structure Correlations in MIR and NIR Spectra of Crystalline Menadione (Vitamin K3).

Mid-infrared (MIR) and near-infrared (NIR) spectra of crystalline menadione (vitamin K3) were measured and analyzed with aid of quantum chemical calculations. The calculations were carried out using the harmonic approach for the periodic model of crystal lattice and the anharmonic DVPT2 calculations applied for the single molecule model. The theoretical spectra accurately reconstructed the experimental ones permitting for reliable assignment of the MIR and NIR bands. For the first time, a detailed analysis of the NIR spectrum of a molecular system based on a naphthoquinone moiety was performed to elucidate the relationship between the chemical structure of menadione and the origin of the overtones and combination bands. In addition, the importance of these bands during interpretation of the MIR spectrum was demonstrated. The overtones and combination bands contribute to 46.4% of the total intensity of menadione in the range of 3600-2600 cm-1. Evidently, these bands play a key role in shaping of the C-H stretching region of MIR spectrum. We have shown also that the spectral regions without fundamentals may provide valuable structural information. For example, the theoretical calculations reliably reconstructed numerous overtones and combination bands in the 4000-3600 and 2800-1800 cm-1 ranges. These results, provide a comprehensive origin of the fundamentals, overtones and combination bands in the NIR and MIR spectra of menadione, and the relationship of these spectral features with the molecular structure.

Green Multi-Platform Solution for the Quantification of Levodopa Enantiomeric Excess in Solid-State Mixtures for Pharmacological Formulations.

The aim of the present work was to develop a green multi-platform methodology for the quantification of l-DOPA in solid-state mixtures by means of MIR and NIR spectroscopy. In order to achieve this goal, 33 mixtures of racemic and pure l-DOPA were prepared and analyzed. Once spectra were collected, partial least squares (PLS) was exploited to individually model the two different data blocks. Additionally, three different multi-block approaches (mid-level data fusion, sequential and orthogonalized partial least squares, and sequential and orthogonalized covariance selection) were used in order to simultaneously handle data from the different platforms. The outcome of the chemometric analysis highlighted the quantification of the enantiomeric excess of l-DOPA in enantiomeric mixtures in the solid state, which was possible by coupling NIR and PLS, and, to a lesser extent, by using MIR. The multi-platform approach provided a higher accuracy than the individual block analysis, indicating that the association of MIR and NIR spectral data, especially by means of SO-PLS, represents a valid solution for the quantification of the l-DOPA excess in enantiomeric mixtures.

Preliminary study on the potential application of Fourier-transform mid-infrared for the evaluation of overall quality and authenticity of Moroccan virgin olive oil.

BACKGROUND: Olive oil provides a wide range of health-promoting compounds. The quality of olive oil is an even more complex concept as it is affected by several factors, such as variety, season, stage of maturation, extraction processing, and so on. The main objective of this study was to determine the potential of chemical and mid-infrared spectroscopy techniques to determine the quality and authenticity of virgin olive oil (VOO). For this, we studied 41 VOOs originating from five regions of Morocco (Fez/Meknes, Eastern, Northern, Beni-Mellal/Khenifra, and Marrakech/Safi) and produced using different agricultural and technological conditions during two successive crop seasons (2015-2016 and 2016-2017). RESULTS: By applying principal component analysis and factorial discriminant analysis with leave-one-out validation to the mid-infrared spectroscopy, clear discrimination between VOO samples according to their geographic origin and variety was observed, with correct classification rates of 91.87% and 91.87% being observed respectively. The application of partial least-squares regression to mid-infrared and chemical data sets allowed excellent prediction of free acidity, peroxide value, k270 , and chlorophyll level with R2 of 0.99, 0.97, 0.98, and 0.93 respectively, and good prediction of k232 (R2 = 0.84). CONCLUSION: The results demonstrate that mid-infrared spectroscopy coupled with chemometric tools could be used as a rapid screening tool for evaluating the overall quality and authenticity of VOO. © 2020 Society of Chemical Industry.

Determination of developmental and ripening stages of whole tomato fruit using portable infrared spectroscopy and Chemometrics.

BACKGROUND: Development and ripening of tomato (Solanum lycopersicum) fruit are important processes for the study of crop biology related to industrial horticulture. Versatile uses of tomato fruit lead to its harvest at various points of development from early maturity through to red ripe, traditionally indicated by parameters such as size, weight, colour, and internal composition, according to defined visual 'grading' schemes. Visual grading schemes however are subjective and thus objective classification of tomato fruit development and ripening are needed for 'high-tech' horticulture. To characterize the development and ripening processes in whole tomato fruit (cv. Moneymaker), a biospectroscopy approach is employed using compact portable ATR-FTIR spectroscopy coupled with chemometrics. RESULTS: The developmental and ripening processes showed unique spectral profiles, which were acquired from the cuticle-cell wall complex of tomato fruit epidermis in vivo. Various components of the cuticle including Cutin, waxes, and phenolic compounds, among others, as well as from the underlying cell wall such as celluloses, pectin and lignin like compounds among others. Epidermal surface structures including cuticle and cell wall were significantly altered during the developmental process from immature green to mature green, as well as during the ripening process. Changes in the spectral fingerprint region (1800-900 cm- 1) were sufficient to identify nine developmental and six ripening stages with high accuracy using support vector machine (SVM) chemometrics. CONCLUSIONS: The non-destructive spectroscopic approach may therefore be especially useful for investigating in vivo biochemical changes occurring in fruit epidermis related to grades of tomato during development and ripening, for autonomous food production/supply chain applications.

ATR-FTIR spectroscopy non-destructively detects damage-induced sour rot infection in whole tomato fruit.

MAIN CONCLUSION: ATR-FTIR spectroscopy with subsequent multivariate analysis non-destructively identifies plant-pathogen interactions during disease progression, both directly and indirectly, through alterations in the spectral fingerprint. Plant-environment interactions are essential to understanding crop biology, optimizing crop use, and minimizing loss to ensure food security. Damage-induced pathogen infection of delicate fruit crops such as tomato (Solanum lycopersicum) are therefore important processes related to crop biology and modern horticulture. Fruit epidermis as a first barrier at the plant-environment interface, is specifically involved in environmental interactions and often shows substantial structural and functional changes in response to unfavourable conditions. Methods available to investigate such systems in their native form, however, are limited by often required and destructive sample preparation, or scarce amounts of molecular level information. To explore biochemical changes and evaluate diagnostic potential for damage-induced pathogen infection of cherry tomato (cv. Piccolo) both directly and indirectly, mid-infrared (MIR) spectroscopy was applied in combination with exploratory multivariate analysis. ATR-FTIR fingerprint spectra (1800-900 cm-1) of healthy, damaged or sour rot-infected tomato fruit were acquired and distinguished using principal component analysis and linear discriminant analysis (PCA-LDA). Main biochemical constituents of healthy tomato fruit epidermis are characterized while multivariate analysis discriminated subtle biochemical changes distinguishing healthy tomato from damaged, early or late sour rot-infected tomato indirectly based solely on changes in the fruit epidermis. Sour rot causing agent Geotrichum candidum was detected directly in vivo and characterized based on spectral features distinct from tomato fruit. Diagnostic potential for indirect pathogen detection based on tomato fruit skin was evaluated using the linear discriminant classifier (PCA-LDC). Exploratory and diagnostic analysis of ATR-FTIR spectra offers biological insights and detection potential for intact plant-pathogen systems as they are found in horticultural industries.

Genome-wide association analysis for ß-hydroxybutyrate concentration in Milk in Holstein dairy cattle.

BACKGROUND: Ketosis in dairy cattle has been shown to cause a high morbidity in the farm and substantial financial losses to dairy farmers. Ketosis symptoms, however, are difficult to identify, therefore, the amount of ketone bodies (mainly ß-hydroxybutyric acid, BHB) is used as an indicator of subclinical ketosis in cows. It has also been shown that milk BHB concentrations have a strong correlation with ketosis in dairy cattle. Mid-infrared spectroscopy (MIR) has recently became a fast, cheap and high-throughput method for analyzing milk components. The aim of this study was to perform a genome-wide association study (GWAS) on the MIR-predicted milk BHB to identify genomic regions, genes and pathways potentially affecting subclinical ketosis in North American Holstein dairy cattle. RESULTS: Several significant regions were identified associated with MIR-predicted milk BHB concentrations (indicator of subclinical ketosis) in the first lactation (SCK1) and second and later lactations (SCK2) in Holstein dairy cows. The strongest association was located on BTA6 for SCK1 and BTA14 on SCK2. Several SNPs on BTA6 were identified in regions and variants reported previously to be associated with susceptibility to ketosis and clinical mastitis in Jersey and Holstein dairy cattle, respectively. One highly significant SNP on BTA14 was found within the DGAT1 gene with known functions on fat metabolism and inflammatory response in dairy cattle. A region on BTA6 and three SNPs on BTA20 were found to overlap between SCK1 and SCK2. However, a novel region on BTA20 (55-63 Mb) for SCK2 was also identified, which was not reported in previous association studies. Enrichment analysis of the list of candidate genes within the identified regions for MIR-predicted milk BHB concentrations yielded molecular functions and biological processes that may be involved in the inflammatory response and lipid metabolism in dairy cattle. CONCLUSIONS: The results of this study confirmed several SNPs and genes identified in previous studies as associated with ketosis susceptibility and immune response, and also found a novel region that can be used for further analysis to identify causal variations and key regulatory genes that affect clinical/ subclinical ketosis.

Data fusion of GC-IMS data and FT-MIR spectra for the authentication of olive oils and honeys-is it worth to go the extra mile?

The potential benefit of data fusion based on different complementary analytical techniques was investigated for two different classification tasks in the field of foodstuff authentication. Sixty-four honey samples from three different botanical origins and 53 extra virgin olive oil samples from three different geographical areas were analyzed by attenuated total reflection IR spectroscopy (ATR/FT-IR) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS). The obtained datasets were combined in a low-level data fusion approach with a subsequent multivariate classification by principal component analysis-linear discriminant analysis (PCA-LDA) or partial least squares-discriminant analysis (PLS-DA). Performing a back projection of PCA loadings, the influence of variables in the FT-IR spectra (one-dimensional) and the GC-IMS profiles (two-dimensional) on the discrimination was visualized within the original axis of the two data sources. Validation results of the classification models were compared to the results that could be obtained by using the individual data blocks separately. For both the honey and olive oil samples, a decreased cross-validation error rate and more robust model was obtained due to the low-level data fusion. The results show that data fusion is an effective strategy for improving the classification performance, particularly for challenging classification tasks such as the discrimination of olive oils with different geographical origin. Graphical abstract.

Improved quantification of important beer quality parameters based on nonlinear calibration methods applied to FT-MIR spectra.

During the production process of beer, it is of utmost importance to guarantee a high consistency of the beer quality. For instance, the bitterness is an essential quality parameter which has to be controlled within the specifications at the beginning of the production process in the unfermented beer (wort) as well as in final products such as beer and beer mix beverages. Nowadays, analytical techniques for quality control in beer production are mainly based on manual supervision, i.e., samples are taken from the process and analyzed in the laboratory. This typically requires significant lab technicians efforts for only a small fraction of samples to be analyzed, which leads to significant costs for beer breweries and companies. Fourier transform mid-infrared (FT-MIR) spectroscopy was used in combination with nonlinear multivariate calibration techniques to overcome (i) the time consuming off-line analyses in beer production and (ii) already known limitations of standard linear chemometric methods, like partial least squares (PLS), for important quality parameters Speers et al. (J I Brewing. 2003;109(3):229-235), Zhang et al. (J I Brewing. 2012;118(4):361-367) such as bitterness, citric acid, total acids, free amino nitrogen, final attenuation, or foam stability. The calibration models are established with enhanced nonlinear techniques based (i) on a new piece-wise linear version of PLS by employing fuzzy rules for local partitioning the latent variable space and (ii) on extensions of support vector regression variants (𝜖-PLSSVR and ν-PLSSVR), for overcoming high computation times in high-dimensional problems and time-intensive and inappropriate settings of the kernel parameters. Furthermore, we introduce a new model selection scheme based on bagged ensembles in order to improve robustness and thus predictive quality of the final models. The approaches are tested on real-world calibration data sets for wort and beer mix beverages, and successfully compared to linear methods, showing a clear out-performance in most cases and being able to meet the model quality requirements defined by the experts at the beer company. Figure Workflow for calibration of non-Linear model ensembles from FT-MIR spectra in beer production .

Uzara - A quality control perspective of Xysmalobium undulatum.

CONTEXT: Xysmalobium undulatum (L.) Aiton f var. (Asclepiadaceae), commonly known as uzara, is an ethnomedicinally important plant from southern Africa used to treat a variety of ailments. In addition to local use in African Traditional Medicine (ATM), formulations containing uzara have been successfully marketed by a number of pharmaceutical companies. Despite its commercialization, published adequate quality control (QC) protocols are lacking. OBJECTIVE: The study was conducted to develop QC protocols for uzara based on chromatographic and spectroscopic techniques. MATERIALS AND METHODS: High performance thin layer chromatography (HPTLC) and liquid chromatography coupled to mass spectrometry (LC-MS) were used to develop phytochemical fingerprints of ethanolic root extracts of 47 uzara samples collected from eight distinct localities in South Africa. Mid-infrared (MIR) spectroscopy was also explored as a suitable alternative technique for rapid and economic quantification of uzarin. RESULTS: Adequate chromatographic profiles were obtained using both HPTLC and LC-MS analyses. The chromatographic patterns showed qualitative similarities among plants collected from different locations. The levels of uzarin, the major constituent of uzara, were highly variable between locations, ranging from 17.8 to 139.9 mg/g (dry weight). A good coefficient of determination (R(2 )= 0.939) and low root mean square error of prediction (RMSEP = 7.9 mg/g) confirmed the accuracy of using MIR-PLS calibration models for the quantification of uzarin. DISCUSSION AND CONCLUSION: Both HPTLC and LC-MS can be used as tools in developing quality control procedures for uzara. MIR in combination with chemometrics provides a fast alternative method for the quantification of uzarin.

A comprehensive high-throughput FTIR spectroscopy-based method for evaluating the transfection event: estimating the transfection efficiency and extracting associated metabolic responses.

Reporter genes are routinely used in every laboratory for molecular and cellular biology for studying heterologous gene expression and general cellular biological mechanisms, such as transfection processes. Although well characterized and broadly implemented, reporter genes present serious limitations, either by involving time-consuming procedures or by presenting possible side effects on the expression of the heterologous gene or even in the general cellular metabolism. Fourier transform mid-infrared (FT-MIR) spectroscopy was evaluated to simultaneously analyze in a rapid (minutes) and high-throughput mode (using 96-wells microplates), the transfection efficiency, and the effect of the transfection process on the host cell biochemical composition and metabolism. Semi-adherent HEK and adherent AGS cell lines, transfected with the plasmid pVAX-GFP using Lipofectamine, were used as model systems. Good partial least squares (PLS) models were built to estimate the transfection efficiency, either considering each cell line independently (R (2) ≥ 0.92; RMSECV ≤ 2 %) or simultaneously considering both cell lines (R (2) = 0.90; RMSECV = 2 %). Additionally, the effect of the transfection process on the HEK cell biochemical and metabolic features could be evaluated directly from the FT-IR spectra. Due to the high sensitivity of the technique, it was also possible to discriminate the effect of the transfection process from the transfection reagent on KEK cells, e.g., by the analysis of spectral biomarkers and biochemical and metabolic features. The present results are far beyond what any reporter gene assay or other specific probe can offer for these purposes.

The Prediction of Quality Parameters of Craft Beer with FT-MIR and Chemometrics.

Beer is one of the oldest and most known alcoholic beverages whose organoleptic characteristics are the attributes that the consumer seeks, which is why it is essential to ensure proper quality control of the final product. Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis can be an alternative to traditional methods to predict quality parameters in craft beer. This study aims to develop prediction models based on FT-MIR spectroscopy to simultaneously quantify quality parameters (color, specific gravity, alcohol volume, bitterness, turbidity, pH, and total acidity) in craft beer. Additionally, principal component analysis (PCA) was applied, and it was possible to classify craft beer samples according to their style. Partial least squares (PLS1) developed the best predictive model by obtaining higher R2c (0.9999) values and lower standard error of calibration (SEC: 0.01-0.11) and standard error of prediction (SEP: 0.01-0.14) values in comparison to the models developed with the other algorithms. Specific gravity could not be predicted due to the low variability in the values. Validation and prediction with external samples confirmed the predictive capacity of the developed model. By making a comparison to traditional techniques, FT-MIR coupled with multivariate analysis has a higher advantage, since it is rapid (approximately 6 min), efficient, cheap, and eco-friendly because it does not require the use of solvents or reagents, representing an alternative to simultaneously analyzing quality parameters in craft beer.

Enhancing Bioactive Compound Classification through the Synergy of Fourier-Transform Infrared Spectroscopy and Advanced Machine Learning Methods.

Bacterial infections and resistance to antibiotic drugs represent the highest challenges to public health. The search for new and promising compounds with anti-bacterial activity is a very urgent matter. To promote the development of platforms enabling the discovery of compounds with anti-bacterial activity, Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy coupled with machine learning algorithms was used to predict the impact of compounds extracted from Cynara cardunculus against Escherichia coli. According to the plant tissues (seeds, dry and fresh leaves, and flowers) and the solvents used (ethanol, methanol, acetone, ethyl acetate, and water), compounds with different compositions concerning the phenol content and antioxidant and antimicrobial activities were obtained. A principal component analysis of the spectra allowed us to discriminate compounds that inhibited E. coli growth according to the conventional assay. The supervised classification models enabled the prediction of the compounds' impact on E. coli growth, showing the following values for accuracy: 94% for partial least squares-discriminant analysis; 89% for support vector machine; 72% for k-nearest neighbors; and 100% for a backpropagation network. According to the results, the integration of FT-MIR spectroscopy with machine learning presents a high potential to promote the discovery of new compounds with antibacterial activity, thereby streamlining the drug exploratory process.

Identification of Variety and Prediction of Chemical Composition in Cocoa Beans (Theobroma cacao L.) by FT-MIR Spectroscopy and Chemometrics.

Cocoa is rich in polyphenols and alkaloids that act as antioxidants, anticarcinogens, and anti-inflammatories. Analytical methods commonly used to determine the proximal chemical composition of cocoa, total phenols, and antioxidant capacity are laborious, costly, and destructive. It is important to develop fast, simple, and inexpensive methods to facilitate their evaluation. Chemometric models were developed to identify the variety and predict the chemical composition (moisture, protein, fat, ash, pH, acidity, and phenolic compounds) and antioxidant capacity (ABTS and DPPH) of three cocoa varieties. SIMCA model showed 99% reliability. Quantitative models were developed using the PLS algorithm and favorable statistical results were obtained for all models: 0.93 < R2c < 0.98 (R2c: calibration determination coefficient); 0.03 < SEC < 4.34 (SEC: standard error of calibration). Independent validation of the quantitative models confirmed their good predictive ability: 0.93 < R2v < 0.97 (R2v: validation determination coefficient); 0.04 < SEP < 3.59 (SEP: standard error of prediction); 0.08 < % error < 10.35). SIMCA model and quantitative models were applied to five external cocoa samples, obtaining their chemical composition using only 100 mg of sample in less than 15 min. FT-MIR spectroscopy coupled with chemometrics is a viable alternative to conventional methods for quality control of cocoa beans without using reagents, and with the minimum sample preparation and quantity.

Building deep learning and traditional chemometric models based on Fourier transform mid-infrared spectroscopy: Identification of wild and cultivated Gastrodia elata.

To identify wild and cultivated Gastrodia elata quickly and accurately, this study is the first to apply three-dimensional correlation spectroscopy (3DCOS) images combined with deep learning models to the identification of G. elata. The spectral data used for model building do not require any preprocessing, and the spectral data are converted into three-dimensional spectral images for model building. For large sample studies, the time cost is minimized. In addition, a partial least squares discriminant analysis (PLS-DA) model and a support vector machine (SVM) model are built for comparison with the deep learning model. The overall effect of the deep learning model is significantly better than that of the traditional chemometric models. The results show that the model achieves 100% accuracy in the training set, test set, and external validation set of the model built after 46 iterations without preprocessing the original spectral data. The sensitivity, specificity, and the effectiveness of the model are all 1. The results concluded that the deep learning model is more effective than the traditional chemometric model and has greater potential for application in the identification of wild and cultivated G. elata.

Tenderness of PGI "Ternera de Navarra" Beef Samples Determined by FTIR-MIR Spectroscopy.

Understanding meat quality attribute changes during ageing by using non-destructive techniques is an emergent pursuit in the agroindustry research field. Using beef certified samples from the protected geographical indication (PGI) "Ternera de Navarra", the primary goal of this study was to use Fourier transform infrared spectroscopy on the middle infrared region (FTIR-MIR) as a tool for the examination of meat tenderness evolution throughout ageing. Samples of the longissimus dorsi muscle of twenty young bulls were aged for 4, 6, 11, or 18 days at 4 °C. Animal carcass classification and sample proximate analysis were performed to check sample homogeneity. Raw aged steaks were analyzed by FTIR-MIR spectroscopy (4000-400 cm-1) to record the vibrational spectrum. Texture profile analysis was performed using a multiple compression test (compression rates of 20%, 80%, and 100%). Compression values were found to decrease notably between the fourth and sixth day of ageing for the three compression rates studied. This tendency continued until the 18th day for C20. For C80 and C100, there was not a clear change in the 11th and 18th days of the study. Regarding FTIR-MIR as a prediction method, it achieved an R2 lower than 40%. Using principal component analysis (PCA) of the results, the whole spectrum fingerprint was used in the discrimination of the starting and final ageing days with correct maturing time classifications. Combining the PCA treatment together with the discriminant analysis of spectral data allowed us to differentiate the samples between the initial and the final ageing points, but it did not single out the intermediate points.