Fourier Transform Mid-infrared Research Articles

Fourier‐transform mid‐infrared spectroscopy for in vitro protein digestibility measurement of pulse crops

AbstractThe standard method of estimating in vitro protein digestibility, the protein digestibility corrected amino acid score (PDCAAS) assay, does not support the expected workflow of a pulse breeding program. This is mainly due to its low‐throughput design and long processing time (∼16–24 h) per sample. Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy has been developed as a high‐throughput phenotyping tool to estimate protein digestibility in pulses. The mid‐infrared region representing the amide I band (1756.81–1586.27 cm−1) was utilized to perform chemometric modeling with partial least squares regression (PLSR) to estimate in vitro protein digestibility in dry pea (Pisum sativum L.), lentil (Lens culinaris Medik.), and chickpea (Cicer arietinum L.) flours. The root mean square error of predictions of the developed PLSR models for dry pea, lentil, and chickpea were 0.00039, 0.00024, and 0.00017, respectively. Phenotyping with the FT‐MIR approach is more rapid than with the PDCAAS assay and estimates protein digestibility from the flour of a single seed in a shorter time (∼1–2 min). The FT‐MIR approach is robust as the spectroscopic data are consistent and chemically fingerprint this nutritional trait. Accordingly, FT‐MIR can resolve the phenotypic bottleneck of pulse breeding related to in vitro protein digestibility measurements by enabling a high‐throughput phenotyping workflow.

Rapid screening of tuna samples for food safety issues related to histamine content using fourier-transform mid-infrared (FT-MIR) and chemometrics

Biogenic amines (BAs) generally result from the decarboxylation reaction of free amino acids as a result of the activity of different microorganisms. A build-up of these compounds can result in food being spoilt. Therefore, the rapid and precise detection of BAs like histamine is an important task for food safety. This research aimed to explore the potential of Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy combined with chemometric methods to assess histamine in fresh tuna quantitatively. Based on the FT-MIR data, partial least squares regression models for the prediction of histamine were successfully constructed with R2 > 0.90. Machine learning algorithms (partial least squares-discrimination analysis, k-nearest neighbors, and support vector machine) were applied, and excellent discrimination results were achieved based on the limits specified in two different legislations (EU and FDA). The results support the use of a rapid, economic and reliable approach for the discrimination of samples that could pose a health risk to consumers.

Horticultural additives influence peat biogeochemistry and increase short-term CO2 production from peat

Abstract Aims Peat is used as a major ingredient of growing media in horticulture. Peat extracted from bogs can be acidic and low in nutrient availability and is therefore mixed with liming agents, nutrients, surfactants, perlite and so on. This study aims to estimate the rates at which raw peat and the modified peat (‘growing media’) decompose to release carbon dioxide (CO2), to estimate the release of carbon (C) from liming agents and to estimate how peat biogeochemistry is changed. Methods We obtained 28 and 24 samples of raw peat and 24 growing media from four peat extraction companies in Canada. Growing media were treated with horticultural additives. We incubated the samples under laboratory conditions, measuring CO2 production, tracer using $${\updelta }^{13}{\text{C}}$$ δ 13 C -$${{\text{CO}}}_{2}$$ CO 2 , pH, C, nitrogen (N) content and humification indices (HIs) from infrared technology called Fourier transform-mid infrared (FT-MIR). Results C:N ratio, pH, dissolved organic carbon, bulk density and C content differed significantly (P < 0.05) between raw peats and growing media. There was more than a doubling of total $${{\text{CO}}}_{2}$$ CO 2 production from growing media compared to raw peat. HIs show higher values for the growing media, which could result from spectral band shifts in the growing media because of increased cation availability. $${\updelta }^{13}{\text{C}}$$ δ 13 C -$${{\text{CO}}}_{2}$$ CO 2 as a tracer showed an average 22% of the total $${{\text{CO}}}_{2}$$ CO 2 production orginated from added carbonate materials. Conclusion Our results provide the rates (0.15 ± 0.017mgCO2-Cg−1d−1) at which horticultural peat decomposes and on the source of emitted $${{\text{CO}}}_{2}$$ CO 2 . This will improve current estimates CO2 emissions from horticultural peat.

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.

Differentiation of mezcales from four agave species using FT-MIR and multivariate statistical analysis

Fourier Transform Mid-Infrared (FT-MIR) spectroscopy and multivariate statistical analysis were used to differentiate mezcales elaborated with four agave species. The FT-MIR data matrix was subjected to spectral transformations using first and second derivatives. The Partial Least Squares (PLS)-Discriminant Analysis (DA) with the matrix transformed by the first and second derivative allowed the differentiation of mezcales. While Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was more robust when it was analyzed with second-derivative data. Pairwise comparisons by OPLS-DA allowed mezcales to be correctly discriminated, mainly between Agave karwinskii and Agave potatorum (Q2 = 0.654 and p – value < 0.01; R2Y = 0.985 and p-value < 0.01) and between Agave angustifolia and Agave karwinskii (Q2 = 0.563 and p-value = 0.01; R2Y = 0.989 and p-value = 0.01). FT-MIR spectrophotometry and the PLS-Regression (PLS-R) were applied to predict the ethanol percentage (% v/v) of mezcales collected in 2022 based on the PLS-R model previously run on samples evaluated in 2021.

Integrating mid-infrared spectroscopy, machine learning, and graphical bias correction for fatty acid prediction in water buffalo milk.

Buffalo milk, constituting 15% of global production, has higher fatty acids content than Holstein milk. Fourier-transform mid-infrared (FT-MIR) spectroscopy is widely used for dairy analysis, but its application to buffalo milk, with larger fat globules, remains understudied. The ultimate goal of this study is to develop machine learning models based on FT-MIR spectroscopy for predicting fatty acids in buffalo milk and to assess the accuracy of commercial milk analyzers. This research provides a convenient, fast, and environmentally friendly method for detecting the fatty acid composition in buffalo milk. We employed six machine learning algorithms to establish a detection model for 34 fatty acids in buffalo milk. The predictive models demonstrated robust capabilities for high-content fatty acids [C14:0, C15:0, C16:0, C17:0, C18:0, C18:1, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA)], with errors within a 15% range. Traditional FT6000 detection methods exhibited limitations in measuring SFAs and polyunsaturated fatty acids (PUFA). Implementing a mean difference correction of 0.21 for MUFAs and applying regression equations (SFA × 1.0639 + 0.0705; PUFA × 0.5472 + 0.0047) significantly improved measurement accuracy. This study successfully developed a predictive model for fatty acids in Mediterranean buffalo milk based on FT-MIR spectroscopy. Additionally, a correction was applied to the existing measurement device, FT6000, enabling more accurate measurements of fatty acids in buffalo milk. The findings have practical implications for the food industry, offering a faster and more reliable approach to assess and monitor fatty acid composition in buffalo milk, potentially influencing product development and quality control processes. © 2024 Society of Chemical Industry.

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.

Prediction of cholesterol content in gallstones by FTIR spectroscopy coupled with chemometric tools

Gallstones are stone-like deposits that most often develop inside the human gallbladder. In this study, the quantification of cholesterol gallstones was monitored using the Fourier transform mid-infrared (FT-MIR) spectroscopy technique and chemometrics tools. To detect the presence of outliers in the spectral data before developing a prediction model, principal component analysis (PCA) was utilized for qualitative analysis. The application of the partial least square (PLS) calibration models for the quantification of cholesterol demonstrated high-performance capabilities, as indicated by high values of correlation coefficients R2 greater than 0.999, and lower values of root mean square error (RMSE) less than 0.493. According to our results, FT-MIR spectroscopy combined with chemometrics approaches can be used successfully as a simple, quick, and non-destructive method for the quantification of cholesterol in biliary lithiasis.

Energy balance of dairy cows predicted by mid-infrared spectra data of milk using Bayesian approaches

Information on dry matter intake (DMI) and energy balance (EB) at the animal and herd level is important for management and breeding decisions. However, routine recording of these traits at commercial farms can be challenging and costly. Fourier-transform mid-infrared (FT-MIR) spectroscopy is a noninvasive technique applicable to a large cohort of animals that is routinely used to analyze milk components and is convenient for predicting complex phenotypes that are typically difficult and expensive to obtain on a large scale. We aimed to develop prediction models for EB and use the predicted phenotypes for genetic analysis. First, we assessed prediction equations using 4,485 phenotypic records from 167 Holstein cows from an experimental station. The phenotypes available were body weight (BW), milk yield (MY) and milk components, weekly-averaged DMI, and FT-MIR data from all milk samples available. We implemented mixed models with Bayesian approaches and assessed them through 50 randomized replicates of a 5-fold cross-validation. Second, we used the best prediction models to obtain predicted phenotypes of EB (EBp) and DMI (DMIp) on 5 commercial farms with 2,365 phenotypic records of MY, milk components and FT-MIR data, and BW from 1,441 Holstein cows. Third, we performed a GWAS and estimated heritability and genetic correlations for energy content in milk (EnM), BW, DMIp, and EBp using the genomic information available on the cows from commercial farms. The highest correlation between the predicted and observed phenotype (ry,y^) was obtained with DMI (0.88) and EB (0.86), while predicting BW was, as anticipated, more challenging (0.69). In our study, models that included FT-MIR information performed better than models without spectra information in the 3 traits analyzed, with increments in prediction correlation ranging from 5% to 10%. For the predicted phenotypes calculated by the prediction equations and data from the commercial farms the heritability ranged between 0.11 and 0.16 for EnM, DMIp and EBp, and 0.42 for BW. The genetic correlation between EnM and BW was -0.17, with DMIp was 0.40 and with EBp was -0.39. From the GWAS, we detected one significant QTL region for EnM, and 3 for BW, but none for DMIp and EBp. The results obtained in our study support previous evidence that FT-MIR information from milk samples contribute to improve the prediction equations for DMI, BW, and EB, and these predicted phenotypes may be used for herd management and contribute to the breeding strategy for improving cow performance.

Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics.

Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30-40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms. Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable. The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM. Blood samples were obtained from LC (n = 50) and FM (n = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm-1 was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate. An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm-1 enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity. This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.

Characterization of petcoke and derived oxygen-functionalized products accompanied by XRD, FT-MIR and SS-NMR

This work applies Solid-State Nuclear Magnetic Resonance (SS-NMR) to characterize structural and motional aspects of petcoke and its derived humic-like products from alkaline wet oxidation. Heteronuclear dipolar coupling modulation schemes are used to characterize local mobility and structural heterogeneity of condensed aromatic layers of petcoke and its oxygen-functionalized products. The analytical agreements between heteronuclear H–C dipolar modulated and direct polarization SS-NMR schemes, C/O/H elemental analysis, X-ray diffraction and Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy confirm that alkaline wet oxidation of petcoke provides structural heterogeneity and flexibility in direct correlation to an increase of oxygen content, an increase of O/C ratio, reduction of aromatic stack layers, reduction of crystallite size and increase of C–O, and O–H dipoles within hydrophobic aromatic ring frameworks submitted to multiple oxygen functionalization chemical processes. For the first time, a technique is proposed to distinguish between humic and fulvic acids analogs prepared from petcoke alkaline wet oxidation by means of 13C-CPMAS, 1H- and 13C-DPMAS experiments. Fulvic acid analogs with high non-paramagnetic ash content show 13C profiles only by direct polarization, not by equivalent heteronuclear dipolar coupling polarization-transfer.These analytical techniques propose orthogonality amongst characterizing alkaline wet oxidation methodologies applied to petcoke for valorization into humic-like substances.

Imputation of missing milk Fourier transform mid-infrared spectra using existing milk spectral databases: A strategy to improve the reliability of breeding values and predictive models

The use of milk Fourier transform mid-infrared (FT-MIR) spectrometry to develop management and breeding tools for dairy farmers and industry is growing and supported by the availability of numerous new predicted phenotypes to assess the nutritional quality of milk and its technological properties, but also the animal health and welfare status and its environmental fingerprint. For genetic evaluations, having a long-term and representative spectral dairy herd improvement (DHI) database improves the reliabilities of estimated breeding values (EBV) from these phenotypes. Unfortunately, most of the time, the raw spectral data used to generate these estimations are not stored. Moreover, many reference measurements of those phenotypes, needed during the FT-MIR calibration step, are available from past research activities but lack spectra records. So, it is impossible to use them to improve the FT-MIR models. Consequently, there is a strong interest in imputing those missing spectra. The innovative objective of this study was to use the existing large spectral DHI database to estimate missing spectra by selecting probable spectra using, as the match criteria, common dairy traits recorded for a long time by DHI organizations. We tested 4 match criteria combinations. Combination 1 required to have equal fat and protein contents between the sample for which a spectrum was to be estimated and the reference samples in the DHI database. Combination 2 also required an equal urea content. Combination 3 requested equal fat, protein, and lactose contents. Finally, combination 4 included all criteria. When more than one spectrum was found during the search, their average was the estimated spectrum for the query sample. Concretely, this study estimated missing spectra for 1,700 samples using 2,000,000 spectral DHI records. For assessing the effect of this spectral estimation on the prediction quality, FT-MIR equations were used to predict 11 phenotypes, selected as their quantification used different FT-MIR regions. They were related to the milk fat and mineral composition, lactoferrin content, quantity of eructed methane, body weight (BW), and dry matter intake. The accuracy between predictions obtained from actual and estimated spectra was evaluated by calculating the mean absolute error (MAE). The criteria in the fourth and second combinations were too strict to estimate a spectrum for most samples. Indeed, for many samples, no spectra with the same values for those matching criteria was found. The third match criteria combination had a poorer prediction performance for all studied traits and spectral absorptions than the first combination due to fewer matched samples available to compute the missing spectrum. By allowing a range for matching lactose content (±0.1 g/dL milk), we showed that this new combination increased the number of selected samples to compute missing spectra and predict better the infrared absorption at different wavenumbers, especially those related to the lactose quantification. The prediction performance was further improved by performing queries on the entire Walloon DHI spectral database (6,625,570 spectra), and it varied among the studied phenotypes. Without considering the traits used for the matching, the best predictions were obtained for the content of saturated fatty acids (MAE = 0.15 g/dL milk) and BW (MAE = 12.80 kg). Yet, the predictions for the unsaturated fatty acids were less accurate (MAE = 0.13 and 0.018 g/dL milk for monounsaturated and polyunsaturated fatty acids), likely because of the poorer predictions of spectral regions related to long-chain fatty acids. Similarly, poorer predictions were observed for the amount of methane eructed by dairy cows (MAE = 47.02 g/d), likely because it is not directly related to fat content or composition. Prediction accuracies for the remaining traits were also low. In conclusion, we observed that increasing the number of relevant matching criteria helps improve the quality of FT-MIR predicted phenotypes and the number of spectra used during the search. So, it would be of great interest to test in the future the suitability of the developed methodology with large-scale international spectral databases to improve the reliability of EBV from these FT-MIR-based phenotypes and the robustness of FT-MIR predictive models.

One-year variation in quantity and properties of microplastics in mussels (Mytilus galloprovincialis) and cockles (Cerastoderma edule) from Aveiro lagoon

As filter feeders, marine bivalves inhabiting estuarine and coastal areas are directly exposed to microplastics (MPs) in water. To assess whether MPs number, and their shape, size, colour, and polymer type present in mussels (Mytilus galloprovincialis) and cockles (Cerastoderma edule) varied over one year, bivalves were collected over the year of 2019 in the lower part of the coastal Aveiro lagoon, Portugal. After extraction from the bivalve's whole-body soft tissues, a subset of the visually inspected particles was randomly separated for identification using the Fourier-transform mid-infrared (FT-MIR) spectroscopy. A fraction of the inspected particles, 26–32% of particles >100 μm, and 59–100% of smaller ones were confirmed as MPs. Concentrations varied within the intervals of 0.77–4.3 items g−1 in mussels and 0.83–5.1 items g−1 in cockles, with the lowest values observed in January. In winter, the accumulation of large-sized fibers was composed of a mixture of plastic types, which contrasted against the most abundant MPs in summer consisting mainly of polyethylene of diverse size classes and shapes. Temperature decrease registered in winter might have triggered a lower filtration rate, resulting in lower MPs concentrations in the whole-soft body tissues of organisms. Different properties of MPs found in bivalves between January–February and August–September appear to reflect changes in the characteristics of MPs available in the Aveiro lagoon.

Effects of fine grinding on mid-infrared spectroscopic analysis of plant leaf nutrient content

Fourier transform mid infrared (FT-MIR) spectroscopy combined with modeling techniques has been studied as a useful tool for multivariate chemical analysis in agricultural research. A drawback of this method is the sample preparation requirement, in which samples must be dried and fine ground for accurate model calibrations. For research involving large sample sets, this may dramatically increase the time and cost of analysis. This study investigates the effect of fine grinding on model performance using leaf tissue from a variety of crop species. Dried leaf samples (N = 300) from various environmental conditions were obtained with data on 11 nutrients measured using chemical methods. The samples were scanned with attenuated total reflectance (ATR) and diffuse reflectance (DRIFT) FT-MIR techniques. Scanning was repeated after fine grinding for 2, 5, and 10 min. The spectra were analyzed for the 11 nutrients using partial least squares regression with a 75%/25% split for calibration and validation and repeated for 50 iterations. All analytes except for boron, iron, and zinc were well-modeled (average R2 > 0.7), with higher R2 values on ATR spectra. The 5 min level of fine grinding was found to be most optimal considering overall model performance and sample preparation time.

Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by β-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CVr) and herd-out (CVh). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R2 value from 5% to 75% for CVr and 2% to 139% for CVh, whereas the stacking ensemble increased the R2 value from 4% to 70% for CVr and 4% to 150% for CVh. Considering the best model, with the CVr scenario, good prediction accuracies were obtained for glucose (R2 = 0.81), urea (R2 = 0.73), albumin (R2 = 0.75), total reactive oxygen metabolites (R2 = 0.79), total thiol groups (R2 = 0.76), ceruloplasmin (R2 = 0.74), total proteins (R2 = 0.81), globulins (R2 = 0.87), and Na (R2 = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.

Saline soil organic matter characteristics of aggregate size fractions after amelioration through straw and nitrogen addition

AbstractSaline soil organic matter (SOM) composition and distribution are largely unknown. A coastal field experiment was designed to investigate the effects of straw and nitrogen addition on SOM characteristics by diffuse reflectance Fourier‐transform mid‐infrared (FTIR) spectral and amino sugar analysis. In each growing season, maize/wheat straw was applied at rates of 5.0 × 103 kg ha−1 (S5) and 1.0 × 104 kg ha−1 (S10), and inorganic nitrogen was applied at rates of 75 kg ha−1 (N75), 150 kg ha−1 (N150), and 300 kg ha−1 (N300). N150 without straw addition was the control treatment (CK). Dry‐sieving technique was used to fractionate soils into macroaggregates (>0.25 mm, MA), microaggregates (0.053–0.25 mm, MI), and silt‐plus‐clay particles (<0.053 mm, SC). Results showed that SOM and amino sugar contents were efficiently increased in the S5 and S10 treatments compared with CK, which were significantly higher in MA and MI than in SC (p < 0.05). In straw‐addition treatments, SOM and amino sugar contents were significantly higher in S5N300 and S10N300 (p < 0.05). The contribution of microbial necromass C to soil organic C (SOC) was 8.9–17.1%, and the fungal residue was dominant in bulk and aggregate soils. The amount of recalcitrant form of organic C decreased with SOM content increase, and the abundance of aromatic C groups increased under higher salt stress. Therefore, we suggested that microbial necromass accumulation could not be the main pathway of SOC sequestration, and enriching recalcitrant organic C probably led to SOM stability in saline soils.

Toward the Non-Targeted Detection of Adulterated Virgin Olive Oil with Edible Oils via FTIR Spectroscopy & Chemometrics: Research Methodology Trends, Gaps and Future Perspectives.

Fourier-Transform mid-infrared (FTIR) spectroscopy offers a strong candidate screening tool for rapid, non-destructive and early detection of unauthorized virgin olive oil blends with other edible oils. Potential applications to the official anti-fraud control are supported by dozens of research articles with a "proof-of-concept" study approach through different chemometric workflows for comprehensive spectral analysis. It may also assist non-targeted authenticity testing, an emerging goal for modern food fraud inspection systems. Hence, FTIR-based methods need to be standardized and validated to be accepted by the olive industry and official regulators. Thus far, several literature reviews evaluated the competence of FTIR standalone or compared with other vibrational techniques only in view of the chemometric methodology, regardless of the inherent characteristics of the product spectra or the application scope. Regarding authenticity testing, every step of the methodology workflow, and not only the post-acquisition steps, need thorough validation. In this context, the present review investigates the progress in the research methodology on FTIR-based detection of virgin olive oil adulteration over a period of more than 25 years with the aim to capture the trends, identify gaps or misuses in the existing literature and highlight intriguing topics for future studies. An extensive search in Scopus, Web of Science and Google Scholar, combined with bibliometric analysis, helped to extract qualitative and quantitative information from publication sources. Our findings verified that intercomparison of literature results is often impossible; sampling design, FTIR spectral acquisition and performance evaluation are critical methodological issues that need more specific guidance and criteria for application to product authenticity testing.

Predicting soil carbon in granitic soils using Fourier-transform mid-infrared (FT-MIR) spectroscopy: the value of database disaggregation

Soil carbon (C) is an important component in quality assessments and efficient models are required to estimate C rapidly. Accurate C assessments are valuable in monitoring land-use changes. Fourier-transform mid-infrared (FT-MIR) spectroscopy has proved to be a powerful tool for assessing C. The potential of FT-MIR spectroscopy to estimate C was evaluated using the following techniques: (1) three algorithms [partial least squares (PLS)], principal component regression (PCR), and classical least squares (CLS); and (2) disaggregating the dataset into subgroups based on soil depth and texture. The C contents of samples collected in the Johannesburg Granite Dome were determined by dry combustion for comparison. The soils ranged considerably in C (0.123–2.650%), clay (2.80–41.20%), and silt content (8.56–23.75%). Using standard normal variant (SNV), Savitzky-Golay smoothing and PLS, the best-performing model was the horizons subgroup which provided values of root mean square error for prediction (RMSEP) between 0.079 and 0.095%, root mean square error for calibration (RMSEC) = 0.041 − 0.092, r 2 pre = 0.6174–0.8459, r 2 cal = 0.6599–0.9778, residual prediction variation (RPD) = 2.404–2.753, and ratio of performance to interquartile range (RPIQ) = 2.667–3.454. The pronounced accuracy of FT-MIR spectroscopy coupled with PLS, pre-processing techniques, and textural subgroups confirms the potential of infrared spectroscopy as an efficient tool for estimating C content. Future studies should investigate the combined effects of FT-MIR spectroscopy and subgroups grouped according to soil types and land-uses when predicting C.

Development of fast analytical method for the detection and quantification of Moroccan picholine extra virgin olive oil adulteration using MIR spectroscopy and chemometrics tools

In this study, the adulteration of Moroccan Picholine extra virgin olive oil with Arbequina virgin olive oil was monitored using the Fourier transform mid-infrared (FT-MIR) spectroscopy technique and chemometrics methodologies. To discriminate between olive oil that has been adulterated and unadulterated, principal component analysis (PCA) was utilized for qualitative analysis. We created the best calibration models for quantitative analysis using principal component regression (PCR) and partial least-squares regression (PLS). The first three principal components account for 95% of the overall variability, according to PCA analysis. PCA allows for the classification of the dataset into two groups: adulterated and unadulterated Moroccan Picholine olive oil. The application of the PLS and PCR calibration models for the quantification of adulteration demonstrates high-performance capabilities, as indicated by high values of correlation coefficients R2 greater than 0.999 and 0.995 and lower values of root mean square error (RMSE) less than 0.767 and 2.16 using PLS and PCR, respectively. According to our results, FT-MIR spectroscopy combined with chemometrics approaches can be used successfully as a simple, quick, and non-destructive method for the quantification and discrimination of adulterated olive oil.

Downhole FT-MIR Spectrometer Using Perfect Metasurface Absorber

Realization of “Fourier transform infrared (FT-IR) on a chip” holds the potential of a disruptive technology for downhole chemical analysis within the oil and gas industry. One of the critical obstacles to downhole integration though has been the cooling requirements of conventional technologies. Here, we report the design and numerical analysis of an uncooled miniaturized Fourier transform mid-infrared (FT-MIR) spectrometer compatible with downhole thermal environments, enabled by a broadband mid-infrared metasurface detector/source combination derived from a geometric inversion of a set of conformal mapping contours. The metasurface is numerically found to exhibit a near-zero index metamaterial (NZIM) behavior with absorption characterized by surface plasmon resonances confined to the ultrathin ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> /300) metasurface plane, making the absorption properties of the microbolometer design much less sensitive to the remaining support structure than in typical designs. This feature allows the metasurface to be integrated on a single VO2 substrate operated at elevated downhole temperatures that coincide with the metal–insulator transition region. Within this transition region, the VO2 material exhibits enhanced thermometric properties, enabling an uncooled microbolometer design with predicted maximum detectivity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${D}^{*} = 1.5\times {10}^{{10}}\,\,\text {cm} \sqrt {\text {Hz}}/\text {W}$ </tex-math></inline-formula> and noise equivalent difference temperature (NEDT) of 1 mK at a modulation frequency of 500 Hz. These parameters approach entry-level lab FT-MIR spectrometers and could represent a significant step in deploying mid-infrared spectroscopy into oilfield downhole logging applications.