Simple and rapid determination of free fatty acids in brown rice by FTIR spectroscopy in conjunction with a second-derivative treatment

Quantitative analysis and visualization of chemical compositions during shrimp flesh deterioration using hyperspectral imaging: A comparative study of machine learning and deep learning models

The current work explores hyperspectral imaging (HSI) to quantitatively identify changes in TVB-N and K value during shrimp flesh deterioration. The work developed low-level data fusion (LLF) and predictive models using both machine learning methods (PLS) and deep learning methods (CNN, LSTM, CNN-LSTM). Results indicate that deep learning methods show comparable performance due to their superior feature extraction and fitting capabilities, but traditional chemometric methods outperform deep learning models, achieving Rp2 = 0.9431 (TVB-N), and Rp2 = 0.9815 (K value). Subsequently, spatial distribution maps were generated based on the optimal predictive models to visualize the chemical composition changes in shrimp flesh. This approach allows for rapid, non-destructive prediction of spoilage-related changes. This technology can monitor shrimp quality in cold chain logistics, improve inventory management, and ensure seafood quality. Future research should optimize models for varied conditions and explore combining HSI method with other sensor technologies to enhance shrimp quality evaluation comprehensively and accurately.

Determination of starch digestibility in white sweet potato after acid hydrolysis and heat-moisture treatment using terahertz spectroscopy

Acid hydrolysis and heat-moisture treatment (HMT) have been used to alter digestibility of starch in white sweet potato, providing health benefits. The potential of terahertz spectroscopy for rapid and non-destructive determination of starch digestibility in white sweet potato treated with acetic acid and citric acid, followed by HMT, was investigated. An in vitro starch digestibility assay was employed to measure the contents of rapidly digestible starch (RDS), slowly digestible starch (SDS), and resistant starch (RS). The intensities of the peaks attributed to covalent vibrational modes at 9.0, 10.5, and 13.1 THz showed a strong correlation with RDS content (R2 > 0.95). Moreover, the intensities of the peaks sensitive to non-covalent forces within and between double helices of starch, around 5.0 and 7.9 THz, were correlated with RS content (R2 > 0.65). These results indicate, after acid hydrolysis and HMT, the starch digestibility of white sweet potato can be determined using THz peaks.

Immunomodulatory and osteogenic effects of chitosan-based injectable hydrogel with geniposide-loaded mesoporous bioactive glass

The immune response dominated by macrophages plays a pivotal role in the regeneration of bone tissue. In this work, an injectable temperature-responsive hydrogel composed of geniposide-loaded mesoporous bioactive glass, chitosan and β-glycerophosphate (G-M Gel) was prepared, showing robustly networks, uniform pore structure, excellent biocompatibility, immunomodulatory effect and osteogenic potential. In an inflammatory microenvironment elicited by lipopolysaccharide (LPS), the proportion of M1 and M2 macrophages measured by flow cytometry were 33.17 % and 2.07 %, respectively. After G-M Gel treatment, the proportion of M1 macrophages decreased to 14.4 %, while the proportion of M2 macrophages increased significantly to 16.2 %. LPS treated macrophage conditioned medium inhibited the expression of osteogenic related factors (OCN, OPN, Runx2), alkaline phosphatase (ALP) and alizarin red S (ARS) in MC3T3-E1 cells. In contrast, LPS + G-M Gel treated macrophage conditioned medium significantly increased the expression of osteogenic related factors, ALP and ARS. These results demonstrated that G-M Gel can augment bone formation by promoting the polarization of M2 macrophages, showing great potential clinical application of G-M Gel in bone regeneration field.

Determination of free fatty acids in Antarctic krill meals based on matrix solid phase dispersion

Amino-modified mesoporous silicawas prepared by modifying mesoporous silica with 3-aminopropyltriethoxysilane and used as adsorbents in matrix solid-phase dispersion (MSPD) to analyze free fatty acids (FFAs) in krill meals for the first time. The adsorption-desorption experiments and Fourier-transform infrared spectroscopy showed amino-modified mesoporous silica with ordered mesoporous structure was successfully synthesized. The adsorption experiments including static and dynamic adsorption showed thatabsorption capacity of amino-modified mesoporous silica towards FFAs was better than that of aminated silicon microspheres at all concentrations. Under optimal extraction conditions, outstanding linearity (0.1-12000 nmol g^-1), low LODs (0.05-1.25 nmol g^-1), satisfactory recoveries (82.17-96.43%) and precisions (0.19-5.26%) were obtained. Moreover, the application of MSPD for FFAs analysis avoided complicated lipid extraction procedures and accomplished the homogenization, crushing, extraction and cleaning of the samples in one step. Consequently, this approach provides an alternative choice to the existing approach for analyzing FFAs in solid and semi-solid samples.

Discrimination of commercial ibuprofen tablets by using a button sample holder and mid-infrared spectroscopy

Infrared spectra obtained from commercial ibuprofen tablets are categorized by using principal component analysis. A stainless steel abrasive button is used to sample the coatings and interiors of ibuprofen tablets with three different formulations. Rubbing the button wire mesh surface across tablet surfaces removes material for analysis. Small fragments are retained within mesh void spaces and larger fragments are swept away prior to analyses. Infrared spectra for tablet coatings exhibit significant differences and can be used for identification. Tablet interior compositions consisting primarily of ibuprofen from different manufacturers are more distinguishable from pre-processed spectra than from spectrum second derivatives. The speed and sensitivity afforded by this methodology suggests that rapid detection of counterfeit pharmaceuticals based on mid-infrared spectroscopy measurements of microgram quantities of material removed with a button sample holder is feasible.

A feasibility quantitative analysis of free fatty acids in polished rice by fourier transform near-infrared spectroscopy and chemometrics

Free fatty acids (FFAs) are an important indicator of the freshness and quality of rice. In this study, the vibration response of C-H chemical bonds (-CH₃ , -CH₂ , H-C = C-H) of FFAs in the near-infrared region was determined by analyzing the standard reagent. In addition, the spectral data of different physical forms of rice and chemometrics, such as partial least squares (PLS), synergy interval-PLS, and competitive adaptive reweighted sampling (CARS), were applied to develop an optimal regression model for rice FFAs determination. The performance of the FFAs model established by using the polished rice granule spectrum (PRG) combined with CARS was the best, the correlation coefficients of the calibration set and prediction set were 0.99 (root mean squared errors of the calibration = 2.00 mg/100 g) and 0.98 (root mean squared errors of the prediction = 3.21 mg/100 g), respectively, and the ratio of performance-to-deviation was 4.50. Compared with the rice powder spectral, the PRG spectral can better retain the information of FFAs. The result shows that NIRS can rapidly, non-destructively, and accurately detect FFAs in rice granules, which will help rice business and food regulatory authorities to establish an early warning mechanism of rice aging. PRACTICAL APPLICATION: Free fatty acids (FFAs) in rice are an important indicator for evaluating the freshness of rice, and their high responsiveness to the deterioration of rice quality. The real-time detection of FFAs in rice can timely adjust the parameters of the rice storage environment, which is very meaningful to ensure the quality of rice.

Evaluation of rice varieties using LIBS and FTIR techniques associated with PCA and machine learning algorithms

Laser-induced breakdown spectroscopy (LIBS) for atomic multi-elementary analyses, and Fourier transform infrared spectroscopy (FTIR) for molecular identification, are often suggested as the most versatile spectroscopic techniques. The present work aimed to evaluate the performance of both techniques, LIBS and FTIR, combined with principal component analysis (PCA) and machine learning (ML) algorithms in the detection of the composition analysis and differentiation of four different types of rice, white, brown, black, and red. The two techniques were primarily used to obtain the elemental and molecular qualitative characterization of rice samples. Then, LIBS and FTIR data sets were subjected to PCA and supervised ML analysis to investigate which main chemical features were responsible for nutritional differences for the white (milled) and colored rice samples. In particular, PCA data analysis suggested that protein, fatty acids, and magnesium were the highest contributors to the sample's differentiation. The ML analysis based on this information yielded a 100% level of accuracy, sensitivity, and specificity on sample classification. In conclusion, LIBS and FTIR coupled with multivariate analysis were confirmed as promising tools alternative to traditional analytical techniques for composition analysis and differentiation when subtle chemical variations were observed.

Non-Destructive Monitoring of P. fluorescens and S. epidermidis Biofilm under Different Media by Fourier Transform Infrared Spectroscopy and Other Corroborative Techniques

In the present study, the early stage of bacteria biofilm formation has been studied as a function of different nutrients. Infrared spectra of Pseudomonas fluorescens (PF) and Staphylococcus epidermidis (SE), on germanium ATR crystal, were collected under deionized water H2O, phosphate buffered solution (PBS) and PBS with glucose (PBS-G). In H2O, protein bands of PF increased while, no difference in PBS and PBS-G were observed until 135 min. SE strain showed a low sensitivity to PBS composition starting to expose proteins on surfaces after 120 min. SE shows a low polysaccharides increase in H2O while, in bare and enriched PBS their intensity increases after 120 and 75 min. in PBS and PBS-G respectively. PF exhibits a peculiar behavior in H2O where the saccharide bands increased strongly after 100 min, while under all the other conditions, the intensity of polysaccharide bands increased up to the plateau probably because the layer of the biofilm exceeded the penetration capability of FTIR technique. All data suggest that, under lack of nutrients, both the bacteria tend to firmly anchor themselves to the support using proteins.

An investigation on annular cartilage samples for post-mortem interval estimation using Fourier transform infrared spectroscopy

Many attempts have been made to estimate the post-mortem interval (PMI) using bioanalytical methods based on multiple biological samples. Cartilage tissues could be used as an alternative for this purpose because their rate of degradation is slower than that of other soft tissue or biofluid samples. In this study, we applied Fourier transform infrared (FTIR) spectroscopy to acquire bioinformation from human annular cartilages within 30 days post-mortem. Principal component analysis (PCA) showed that sex and causes of death have almost no impact on the overall spectral variations caused by post-mortem changes. With pre-processing approaches, several predicted models were established using a conventional machine learning method, known as the partial least square (PLS) regression. The best model achieved a satisfactory prediction with a low error of 1.49 days using the second derivative transform of 3-point smoothing and extended multiplicative scatter correction (EMSC), and the spectral regions from proteins and carbohydrates contributed greatly to the PMI prediction. This study demonstrates the feasibility of cartilage-based FTIR analysis for PMI estimation. Further work will introduce advanced algorithms for more accurate and precise PMI prediction.

Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Combined with Chemometrics Methods for the Classification of Lingzhi Species

Due to the existence of Lingzhi adulteration, there is a growing demand for species classification of medicinal mushrooms by various techniques. The objective of this study was to explore a rapid and reliable way to distinguish between different Lingzhi species and compare the influence of data pretreatment methods on the recognition results. To this end, 120 fresh fruiting bodies of Lingzhi were collected, and all of them were analyzed by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). Random forest (RF), support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) classification models were established for raw and pretreated second derivative (SD) spectral matrices to authenticate different Lingzhi species. The results of multivariate statistical analysis indicated that the SD preprocessing method displayed a higher classification ability, which may be attributed to the analysis of powder samples that requires removal of overlapping peaks and baseline shifts. Compared with RF, the results of the SVM and PLS-DA methods were more satisfying, and their accuracies for the test set were both 100%. Among SVM and PLS-DA, the training set and test set accuracy of PLS-DA were both 100%. In conclusion, ATR-FTIR spectroscopy data pretreated by SD combined with PLS-DA is a simple, rapid, non-destructive and relatively inexpensive method to discriminate between mushroom species and provide a good reference to quality assessment.

Differentiation of cultivation areas and crop years of milled rice using single grain mass spectrometry

A method for the rapid detection of fatty acids in single rice grain would make the evaluation of rice quality easier.

Spectra-structure correlations of saturated and unsaturated medium-chain fatty acids. Near-infrared and anharmonic DFT study of hexanoic acid and sorbic acid

Quantum chemical reproduction of entire NIR spectra is a new trend, enabled by contemporary advances in the anharmonic approaches. At the same time, recent increase of the importance of NIR spectroscopy of biological samples raises high demand for gaining deeper understanding of NIR spectra of biomolecules, i.e. fatty acids. In this work we investigate saturated and unsaturated medium-chain fatty acids, hexanoic acid and sorbic acid, in the near-infrared region. By employing fully anharmonic density functional theory (DFT) calculations we reproduce the experimental NIR spectra of these systems, including the highly specific spectral features corresponding to the dimerization of fatty acids. Broad range of concentration levels from 5·10^-4M in CCl₄ to pure samples are investigated. The major role of cyclic dimers can be evidenced for the vast majority of these samples. A highly specific NIR feature of fatty acids, the elevation of spectral baseline around 6500-4000cm^-1, is being explained by the contributions of combination bands resulting from the vibrations of hydrogen-bonded OH groups in the cyclic dimers. Based on the high agreement between the calculated and experimental NIR spectra, a detailed NIR band assignments are proposed for hexanoic acid and sorbic acid. Subsequently, the correlations between the structure and NIR spectra are elucidated, emphasizing the regions in which clear and universal traces of specific bands corresponding to saturated and unsaturated alkyl chains can be established, thus demonstrating the wavenumber regions highly valuable for structural identifications.