Groundnut oil is known as a good source of essential fatty acids which are significant in the physiological development of the human body. It has a distinctive fragrant making it ideal for cooking which contribute to its demand on the market. However, some groundnut oil producers have been suspected to produce groundnut oil by blending it with cheaper oils especially palm olein at different concentrations or by adding groundnut flavor to palm olein. Over the years, there have been several methods to detect adulteration in oils which are time-consuming and expensive. Near infrared (NIR) and ultraviolet-visible (UV-Vis) spectroscopies are cheap and rapid methods for oil adulteration. This present study aimed to apply NIR and UV-Vis in combination with chemometrics to develop models for prediction and quantification of groundnut oil adulteration. Using principal component analysis (PCA) scores, pure and prepared adulterated samples showed overlapping showing similarities between them. Linear discriminant analysis (LDA) models developed from NIR and UV-Vis gave an average cross-validation accuracy of 92.61% and 62.14% respectively for pure groundnut oil and adulterated samples with palm olein at 0, 1, 3, 5, 10, 20, 30, 40 and 50% v/v. With partial least squares regression free fatty acid, color parameters, peroxide and iodine values could be predicted with R2CV\'s up to 0.8799 and RMSECV\'s lower than 3 ml/100 ml for NIR spectra and R2CV\'s up to 0.81 and RMSECV\'s lower than 4 ml/100 ml for UV-Vis spectra. NIR spectra produced better models as compared to UV-Vis spectra.

Near-infrared (NIR) fluorescent probes with aggregation-induced emission (AIE) properties are of great significance in cell imaging and cancer therapy. However, the complexity of its synthesis, poor photostabilities, and expensive raw materials still pose some obstacles to their practical application. This study reported an AIE luminescent material with red emission and its application in in vitro imaging and photodynamic therapy (PDT) study. This material has the characteristics of simple synthesis, large Stokes shift, good photostabilities, and excellent lipid droplets-specific testing ability. Interestingly, this red-emitting material can effectively produce reactive oxygen species (ROS) under white light irradiation, further achieving PDT-mediated killing of cancer cells. In conclusion, this study demonstrates a simple approach to synthesize NIR AIE probes with both imaging and therapeutic effects, providing an ideal architecture for constructing long-wavelength emission AIE materials.

Hepatocellular carcinoma (HCC) has emerged as a major contributor to the worldwide cancer burden. Improved methods are needed for early cancer detection and image-guided surgery. Peptides have small dimensions that can overcome delivery challenges to achieve high tumor concentrations and deep penetration. We used phage display methods to biopan against the extra-cellular domain of the purified EpCAM protein, and used IRDye800 as a near-infrared (NIR) fluorophore. The 12-mer sequence HPDMFTRTHSHN was identified, and specific binding to EpCAM was validated with HCC cells in vitro. A binding affinity of kd = 67 nM and onset of k = 0.136 min-1 (7.35 min) were determined. Serum stability was measured with a half-life of T1/2 = 2.6 h. NIR fluorescence images showed peak uptake in vivo by human HCC patient-derived xenograft (PDX) tumors at 1.5 h post-injection. Also, the peptide was able to bind to foci of local and distant metastases in liver and lung. Peptide biodistribution showed high uptake in tumor versus other organs. No signs of acute toxicity were detected during animal necropsy. Immunofluorescence staining of human liver showed specific binding to HCC compared with cirrhosis, adenoma, and normal specimens.

BACKGROUND: Subjective surgeon interpretation of near-infrared perfusion video is limited by low inter-observer agreement and poor correlation to clinical outcomes. In contrast, quantification of indocyanine green fluorescence video (Q-ICG) correlates with histologic level of perfusion as well as clinical outcomes. Measuring dye volume over time, however, has limitations, such as it is not on-demand, has poor spatial resolution, and is not easily repeatable. Laser speckle contrast imaging quantification (Q-LSCI) is a real-time, dye-free alternative, but further validation is needed. We hypothesize that Q-LSCI will distinguish ischemic tissue and correlate over a range of perfusion levels equivalent to Q-ICG.
METHODS: Nine sections of intestine in three swine were devascularized. Pairs of indocyanine green fluorescence imaging and laser speckle contrast imaging video were quantified within perfused, watershed, and ischemic regions. Q-ICG used normalized peak inflow slope. Q-LSCI methods were laser speckle perfusion units (LSPU), the base unit of laser speckle imaging, relative perfusion units (RPU), a previously described methodology which utilizes an internal control, and zero-lag normalized cross-correlation (X-Corr), to investigate if the signal deviations convey accurate perfusion information. We determine the ability to distinguish ischemic regions and correlation to Q-ICG over a perfusion gradient.
RESULTS: All modalities distinguished ischemic from perfused regions of interest; Q-ICG values of 0.028 and 0.155 (p < 0.001); RPU values of 0.15 and 0.68 (p < 0.001); and X-corr values of 0.73 and 0.24 (p < 0.001). Over a range of perfusion levels, RPU had the best correlation with Q-ICG (r = 0.79, p < 0.001) compared with LSPU (r = 0.74, p < 0.001) and X-Corr (r = 0.46, p < 0.001).
CONCLUSIONS: These results demonstrate that Q-LSCI discriminates ischemic from perfused tissue and represents similar perfusion information over a broad range of perfusion levels comparable to clinically validated Q-ICG. This suggests that Q-LSCI might offer clinically predictive real-time dye-free quantification of tissue perfusion. Further work should include validation in histologic studies and human clinical trials.

Multivariate calibration models often encounter challenges in extrapolating beyond the calibration instruments due to variations in hardware configurations, signal processing algorithms, or environmental conditions. Calibration transfer techniques have been developed to mitigate this issue. In this study, we introduce a novel methodology known as Supervised Factor Analysis Transfer (SFAT) aimed at achieving robust and interpretable calibration transfer. SFAT operates from a probabilistic framework and integrates response variables into its transfer process to effectively align data from the target instrument to that of the source instrument. Within the SFAT model, the data from the source instrument, the target instrument, and the response variables are collectively projected onto a shared set of latent variables. These latent variables serve as the conduit for information transfer between the three distinct domains, thereby facilitating effective spectra transfer. Moreover, SFAT explicitly models the noise variances associated with each variable, thereby minimizing the transfer of non-informative noise. Furthermore, we provide empirical evidence showcasing the efficacy of SFAT across three real-world datasets, demonstrating its superior performance in calibration transfer scenarios.

Introduction: Photothermal therapy (PTT) by using a near-infrared (NIR) laser, as a successful treatment of cancer, has attracted extensive attention of researchers. Its advantages as a noninvasive and suitable method have been confirmed. Discovery of the NIR laser molecular mechanism at the cellular level via system biology assessment to identify the crucial targeted genes is the aim of this study. Methods: RNA-seq series of six samples were retrieved from Gene Expression Omnibus (GEO) and pre-evaluated by the GEO2R program for more analysis. The significant differentially expressed genes (DEGs) were determined and studied via gene expression analysis, protein-protein interaction (PPI) network assessment, action map evaluation, and gene ontology enrichment. Results: HSPA5, DDIT3, TRIB3, PTGS2, HMOX1, ASNS, GDF15, SLC7A11, and SQSTM1 were identified as central genes. Comparing the central genes and the determined crucial genes via gene expression analysis, actin map results, and gene ontology enrichment led to the introduction of HSPA5, DDIT3, PTGS2, HMOX1, and GDF15 as critical genes in response to the NIR laser. Conclusion: The results indicated that the principle biological process \"Endoplasmic reticulum unfolded protein response\" and HSPA5, DDIT3, PTGS2, HMOX1, and GDF15 are the crucial targets of the NIR laser. The results also showed that the NIR laser induces stress conditions in the irradiated cells.

A recent study showed the potential of the DA Perten 7200 NIR Spectrometer in detecting chlorpyrifos-methyl pesticide residue in rough, brown, and milled rice. However, this instrument is still lab-based and generally suited for point-of-sale testing. To provide a field-deployable version of this technique, an existing light emitting diode (LED)-based instrument that provides discrete NIR wavelength illumination and reflectance spectra over the range of 850-1550 nm was tested. Spectra were collected from rough, brown, and milled rice at different pesticide concentrations and analyzed for quantitative and qualitative measurement using partial least squares regression (PLS) and discriminant analysis (DA). Simulations for two LED-based instruments were also evaluated using corresponding segments of spectra from the DA7200 to represent LED illumination. For the simulation of the existing LED-based instrument (LEDPrototype1) fitted with 850, 910, 940, 970, 1070, 1200, 1300, 1450, and 1550 nm LED wavelengths, resulting R2 ranged from 0.52 to 0.71, and the correct classification was 70.4% to 100%. The simulation of a second LED instrument (LEDPrototype2) fitted with 980, 1050, 1200, 1300, 1450, 1550, 1600, and 1650 nm LED wavelengths showed R2 of 0.59 to 0.82 and correct classifications of 66% to 100%. These LED wavelengths were selected based on the significant wavelength regions from the PLS regression coefficients of DA7200 and the commercial availability of LED wavelengths. Results showed that it is possible to use a multi-spectral LED-based instrument to detect varying levels of chlorpyrifos-methyl pesticide residue in rough, brown, and milled rice.

To monitor the biological function of H2S in real time, this investigation demonstrated the design and synthesis of a novel fluorescent probe integrated with cyanine and 2,4-dinitrophenol for the qualitative and quantitative detection of H2S. An NIR sensitive sensor (FS-HS-1) was provided with a straightforward process. Spectroscopy experiments elucidated that FS-HS-1 could selectively detect H2S in a PBS solution (containing 40% acetonitrile) with a 111-fold fluorescence enhancement at 715 nm (ex. 605 nm). The response towards NaHS occurred in less than 2 min, and the detection limit was confirmed to be as low as 4.47 ± 0.11 nmol/L. Furthermore, the probe is capable of monitoring changes in exogenous H2S concentrations within living cells with confocal and 2P imaging.

Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport-Beach-Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and stacking ensemble to automatically select suitable base models and associated weights have not been widely explored in hyperspectral anomaly detection, we believe that our work will expand the knowledge in this research area and contribute to the wider application of this approach.

Peanut oil, prized for its unique taste and nutritional value, grapples with the pressing issue of adulteration by cost-cutting vendors seeking higher profits. In response, we introduce a novel approach using near-infrared spectroscopy to non-invasively and cost-effectively identify adulteration in peanut oil. Our study, analyzing spectral data of both authentic and intentionally adulterated peanut oil, successfully distinguished high-quality pure peanut oil (PPEO) from adulterated oil (AO) through rigorous analysis. By combining near-infrared spectroscopy with factor analysis (FA) and partial least squares regression (PLS), we achieved discriminant accuracies exceeding 92 % (S > 2) and 89 % (S > 1) for FA models 1 and 2, respectively. The PLS model demonstrated strong predictive capabilities, with a prediction coefficient (R2) surpassing 93.11 and a root mean square error (RMSECV) below 4.43. These results highlight the effectiveness of NIR spectroscopy in confirming the authenticity of peanut oil and detecting adulteration in its composition.