As the call for an international standard for milk from grassland-based production systems continues to grow, so too do the monitoring and evaluation policies surrounding this topic. Individual stipulations by countries and milk producers to market their milk under their own grass-fed labels include a compulsory number of grazing days per year, ranging from 120 d for certain labels to 180 d for others, a specified amount of herbage in the diet or a prescribed dietary proportion of grassland-based forages (GBF) fed and produced on farm. As these multifarious policy and label requirements are laborious and costly to monitor on farm, fast economical proxies would be advantageous to verify the proportion of GBF consumed by the cows in the final product. With this in mind, we employed readily available mid-infrared spectral data (n = 1132 spectra) from routine milk controls to develop binary classification models for 4 main feed groups from a primarily forage-based diet: Total GBF (≥50% n = 955, ≥ 75% n = 599, ≥ 85% n = 356), pasture (≥20% n = 451, ≥ 50% n = 284, ≥ 70% n = 152), fresh herbage (pasture + fresh herbage indoor feeding, ≥ 20% n = 517, ≥ 50% n = 325, ≥ 70% n = 182) and whole plant corn (fresh + conserved) (≥10% n = 646, ≥ 30% n = 187), the latter as a negative control. We compared 4 machine learning methods to assess which statistical model performs best at discriminating these classes. Three of these models have not yet been tested for herd-level dietary proportion classification and all 4 follow completely different approaches: least absolute shrinkage and selection operator (LASSO), partial least squares discriminant analysis (PLS-DA), random forest (RF) and support vector machines (SVM). Seasonality has been a missing element from previous dietary herbage proportion classification models. As grazing and fresh herbage indoor feeding are highly dependent on the season, we developed an indicator to incorporate seasonality in a consistent, unbiased manner into our models. We also tested 3 sets of covariates. The first set included only mid-infrared spectra derived data, the second included mid-infrared spectra derived data plus seasonality indices and the third included mid-infrared spectra derived data, seasonality indices and additional herd specific information (DIM, breed and parity). Of the 4 machine learning algorithms tested for the binary classification of GBF proportion at herd level, LASSO and PLS-DA performed best according to evaluation metrics; however, the RF and SVM models were not far behind the best performing model evaluation metrics in each feed category. Our best performing model, the LASSO model containing seasonality indices and herd specific information, classified total GBF ≥50% with an accuracy of 78.6%, precision of 85.1%, sensitivity of 90.6%, specificity 14.1% and F1 score (harmonic mean of precision and sensitivity) of 87.7%, this was very similar to the PLS-DA model. Our results suggest that in general LASSO and PLS-DA machine learning algorithms perform better for dietary GBF classification than RF or SVM algorithms.

For the first time, we demonstrate the hybrid integration of dual distributed feedback (DFB) quantum cascade lasers (QCLs) on a silicon photonics platform using an innovative 3D self-aligned flip-chip assembly process. The QCL waveguide geometry was predesigned with alignment fiducials, enabling a sub-micron accuracy during assembly. Laser oscillation was observed at the designed wavelength of 7.2 μm, with a threshold current of 170 mA at room temperature under pulsed mode operation. The optical output power after an on-chip beam combiner reached sub-milliwatt levels under stable continuous wave operation at 15 °C. The specific packaging design miniaturized the entire light source by a factor of 100 compared with traditional free-space dual lasers module. Divergence values of 2.88 mrad along the horizontal axis and 1.84 mrad along the vertical axis were measured after packaging. Promisingly, adhering to i-line lithography and reducing the reliance on high-end flip-chip tools significantly lowers the cost per chip. This approach opens new avenues for QCL integration on silicon photonic chips, with significant implications for portable mid-infrared spectroscopy devices.

OBJECTIVE: Bevacizumab-based chemotherapy is a recommended first-line treatment for metastatic colorectal cancer (mCRC). Robust biomarkers with clinical practice applicability have not been identified for patients with this treatment. We aimed to evaluate the prognostic yield of serum mid-infrared spectroscopy (MIRS) on patients receiving first-line bevacizumab-based chemotherapy for mCRC.
METHODS: We conducted an ancillary analysis from a multicentre prospective study (NCT00489697). All baseline serums were screened by attenuated total reflection method. Principal component analysis and unsupervised k-mean partitioning methods were performed blinded to all patients\' data. Endpoints were progression-free survival (PFS) and overall survival (OS).
RESULTS: From the 108 included patients, MIRS discriminated two prognostic groups. First group patients had significantly lower body mass index (p = 0.026) and albumin levels (p < 0.001), and higher levels of angiogenic markers, lactate dehydrogenase and carcinoembryonic antigen (p < 0.001). In univariate analysis, their OS and PFS were shorter with respective medians: 17.6 vs 27.9 months (p = 0.02) and 8.7 vs 11.3 months (p = 0.03). In multivariate analysis, PFS was significantly shorter (HR = 1.74, p = 0.025) with a similar trend for OS (HR = 1.69, p = 0.061).
CONCLUSIONS: By metabolomic fingerprinting, MIRS proves to be a promising prognostic tool for patients receiving first-line bevacizumab-based chemotherapy for mCRC.

Vegetated coastal ecosystems (VCE), encompassing tidal marshes, mangroves, and seagrasses, serve as significant \'blue\' carbon (C) sinks. Improving our understanding of VCE soils and their spatial and temporal dynamics is essential for conservation efforts. Conventional methods to characterise the dynamics and provenance of VCE soils and measure their total organic carbon (TOC) and inorganic carbon (TIC) contents are cumbersome and expensive. We recorded the mid-infrared (MIR) spectra and measured the TOC and TIC content of 323 subsamples across consistent depths from 106 soil core samples. Using the spectra of each VCE, we determined their mineral and organic composition by depth. We then used a regression tree algorithm, cubist, to model TOC and TIC contents. We rigorously validated the models to test their performance with a 10-fold cross-validation, bootstrapping, and a separate random test dataset. Our analysis revealed distinct mineralogical and organic MIR signatures in VCE soils that correlated with their position within the seascape. The spectra showed decreased clay minerals and increased quartz and carbonate with distance from freshwater inputs. The mineralogy of tidal marsh and mangrove soils differed with depth, showing larger absorptions due to carbonate and quartz and weakening clay minerals and organics absorptions. The mineralogy of the seagrass soils remained the same with depth. The cubist models to estimate TOC and TIC content were accurate (Lin\'s concordance correlation, ρc≥ 0.92 and 0.93 respectively) and interpretable, confirming our understanding of C in these systems. These findings shed light on the provenance of the soils and help quantify the flux and accumulation of TOC and TIC, which is crucial for informing VCE conservation. Moreover, our results indicate that MIR spectroscopy could help scale the measurements cost-effectively, for example, in carbon crediting schemes and to improve inventories. The approach will help advance blue C science and contribute to the conservation and protection of VCE.

This study evaluated the potential use of mid-infrared spectroscopy to predict milk coagulation traits in bulk milk from Mediterranean Italian buffaloes. A total of 1736 bulk milk samples from 55 farms in central Italy were collected during the official milk quality testing system. The prediction models were developed based on modified partial least-squares regression with 75% of the samples and validated with the remaining samples. All bulk milk samples coagulated between 7.37 and 29.45 min. Average values for milk coagulation traits in the calibration set were 17.71 min, 3.29 min, and 38.83 mm for rennet coagulation time, curd firming time, and curd firmness, respectively. The validation set included samples with similar mean and standard deviation for each trait. The prediction models showed the greatest coefficient of determination of external validation (0.57) and the ratio of prediction to deviation (1.52) for curd firmness. Similar fitting statistics of the prediction models were obtained for rennet coagulation time and curd firming time. In conclusion, the prediction models for all three coagulation traits were below the threshold to consider the prediction models adequate even for rough screening of the samples.

In order to improve the ability of clinical diagnosis to differentiate articular cartilage (AC) injury of different origins, this study explores the sensitivity of mid-infrared (MIR) spectroscopy for detecting structural, compositional, and functional changes in AC resulting from two injury types. Three grooves (two in parallel in the palmar-dorsal direction and one in the mediolateral direction) were made via arthrotomy in the AC of the radial facet of the third carpal bone (middle carpal joint) and of the intermediate carpal bone (the radiocarpal joint) of nine healthy adult female Shetland ponies (age = 6.8 ± 2.6 years; range 4-13 years) using blunt and sharp tools. The defects were randomly assigned to each of the two joints. Ponies underwent a 3-week box rest followed by 8 weeks of treadmill training and 26 weeks of free pasture exercise before being euthanized for osteochondral sample collection. The osteochondral samples underwent biomechanical indentation testing, followed by MIR spectroscopic assessment. Digital densitometry was conducted afterward to estimate the tissue\'s proteoglycan (PG) content. Subsequently, machine learning models were developed to classify the samples to estimate their biomechanical properties and PG content based on the MIR spectra according to injury type. Results show that MIR is able to discriminate healthy from injured AC (91%) and between injury types (88%). The method can also estimate AC properties with relatively low error (thickness = 12.7% mm, equilibrium modulus = 10.7% MPa, instantaneous modulus = 11.8% MPa). These findings demonstrate the potential of MIR spectroscopy as a tool for assessment of AC integrity changes that result from injury.

Ongoing technological advancements in the field of mid-infrared (MIR) spectroscopy continuously yield novel sensing modalities, offering capabilities beyond traditional techniques like Fourier transform infrared spectroscopy (FT-IR). One such advancement is MIR dispersion spectroscopy, utilizing a tunable quantum cascade laser and Mach-Zehnder interferometer for liquid-phase analysis. Our study assesses the performance of a custom MIR dispersion spectrometer at its current development stage, benchmarks its performance against FT-IR, and validates its potential for time-resolved chemical reaction monitoring. Unlike conventional methods of IR spectroscopy measuring molecular absorptions using intensity attenuation, our method detects refractive index changes (phase shifts) down to a level of 6.1 × 10-7 refractive index units (RIU). This results in 1.5 times better sensitivity with a sevenfold increase in analytical path length, yielding heightened robustness for the analysis of liquids compared to FT-IR. As a case study, we monitor the catalytic activity of invertase with sucrose, observing the formation of resultant monosaccharides and their progression toward thermodynamic equilibrium. Anomalous refractive index spectra of reaction mixtures, with substrate concentrations ranging from 2.5 to 25 g/L, are recorded, and analyzed at various temperatures, yielding Michaelis-Menten kinetics findings comparable to the literature. Additionally, the first-time application of two-dimensional correlation spectroscopy on the recorded dynamic dispersion spectra correctly identifies the mutarotation of reaction products (glucose and fructose). The results demonstrate high precision and sensitivity in investigating complex time-dependent chemical reactions via broadband refractive index changes.

Mid-infrared (MIR) spectroscopy can characterize the content and structural changes of macromolecular components in different breast tissues, which can be used for feature extraction and model training by machine learning to achieve accurate classification and recognition of different breast tissues. In parallel, the one-dimensional convolutional neural network (1D-CNN) stands out in the field of deep learning for its ability to efficiently process sequential data, such as spectroscopic signals. In this study, MIR spectra of breast tissue were collected in situ by coupling the self-developed MIR hollow optical fiber attenuated total reflection (HOF-ATR) probe with a Fourier transform infrared spectroscopy (FTIR) spectrometer. Staging analysis was conducted on the changes in macromolecular content and structure in breast cancer tissues. For the first time, a trinary classification model was established based on 1D-CNN for recognizing normal, paracancerous and cancerous tissues. The final predication results reveal that the 1D-CNN model based on baseline correction (BC) and data augmentation yields more precise classification results, with a total accuracy of 95.09%, exhibiting superior discrimination ability than machine learning models of SVM-DA (90.00%), SVR (88.89%), PCA-FDA (67.78%) and PCA-KNN (70.00%). The experimental results suggest that the application of 1D-CNN enables accurate classification and recognition of different breast tissues, which can be considered as a precise, efficient and intelligent novel method for breast cancer diagnosis.

Considering the health relevance of Chagas\' disease, recent research efforts have focused on developing more efficient drug delivery systems containing nifurtimox (NFX). This paper comprehensively investigates NFX through conformational analysis and spectroscopic characterization. Using a conformer-rotamer ensemble sampling tool (CREST-xtb), five distinct conformers of NFX were sampled within a 3.0 kcal mol-1 relative energy window. Subsequently, such structures were used as inputs for geometry optimization by density functional theory (DFT) at B3LYP-def2-TZVP level of theory. Notably, harmonic vibrational frequencies were calculated to establish an in-depth comparison with experimental results and existing literature for the NFX or similar molecules and functional groups, thereby achieving a widely reasoned assignment of the mid-infrared band absorptions for the first time. Moreover, UV-VIS spectra of NFX were obtained in several solvents, enabling the determination of the molar absorptivity coefficient for the two electronic transitions observed for NFX. Among the aprotic solvents, a bathochromic effect was observed in the function of the dielectric constants. Furthermore, a hypochromic effect was observed when the drug was dissolved in protic solvents. These findings offer crucial support for new drug delivery systems containing NFX while demonstrating the potential of spectrophotometric studies in establishing quality control assays for NFX drug products.

Caffeine is the most widely consumed stimulant and is the subject of significant ongoing research and discussions due to its impact on human health. The industry\'s need to comply with country-specific food and beverage regulations underscores the importance of monitoring caffeine levels in commercial products. In this study, we propose an alternative technique for caffeine analysis that relies on mid-infrared laser-based photothermal spectroscopy (PTS). PTS exploits the high-power output of the quantum cascade laser (QCL) sources to enhance the sensitivity of the mid-IR measurement. The laser-induced thermal gradient in the sample scales with the analytes\' absorption coefficient and concentration, thus allowing for both qualitative and quantitative assessment. We evaluated the performance of our experimental PTS spectrometer, incorporating a tunable QCL and a Mach-Zehnder interferometer, for detecting caffeine in coffee, black tea, and an energy drink. We calibrated the setup with caffeine standards (0.1-2.5 mg mL-1) and we benchmarked the setup\'s capabilities against gas chromatography (GC) and Fourier-transform infrared (FTIR) spectroscopy. Quantitative results aligned with GC analysis, and limits of detection matched the research-grade FTIR spectrometer, indicating an excellent performance of our custom-made instrument. This method offers an alternative to established techniques, providing a platform for fast, sensitive, and non-destructive analysis without consumables as well as with high potential for miniaturization.