Advanced optical photothermal infrared spectroscopy for comprehensive characterization of microplastics from intravenous fluid delivery systems.

Growing attention is being directed towards exploring the potential harmful effects of microplastic (MP) particles on human health. Previous reports on human exposure to MPs have primarily focused on inhalation, ingestion, transdermal routes, and, potentially, transplacental transfer. The intravenous transfer of MP particles in routine healthcare settings has received limited exploration in existing literature. Standard hospital IV system set up with 0.9 % NaCl in a laminar flow hood with MP contamination precautions. Various volumes of 0.9 % NaCl passed through the system, some with a volumetric pump. Fluid filtered with Anodisc filters washed with isopropyl alcohol. The IV cannula was immersed in Mili-Q water for 72 h to simulate vein conditions. Subsequently, the water was filtered and washed. Optical photothermal infrared (O-PTIR) microspectroscopy is used to examine filters for MP particles. All filters examined from the IV infusion system contained MP particles, including MPs from the polymer materials used in the manufacture of the IV delivery systems (polydimethylsiloxane, polypropylene, polystyrene, and polyvinyl chloride) and MP particles arising from plastic resin additives (epoxy resin, polyamide resin, and polysiloxane-containing MPs). The geometric mean from the extrapolated result data indicated that approximately 0.90 MP particles per mL of 0.9 % NaCl solution can be administered through a conventional IV infusion system in the absence of a volumetric pump. However, with the implementation of a pump, this value may increase to 1.57 particles per mL. Notably, over 72 h, a single cannula was found to release approximately 558 MP particles including polydimethylsiloxane, polysiloxane-containing MPs, polyamide resin, and epoxy resin. Routine IV infusion systems release microplastics. MP particles are also released around IV cannulas, suggesting transfer into the circulatory system during standard IV procedures.

Degradation of Poly(ethylene terephthalate) Catalyzed by Nonmetallic Dibasic Ionic Liquids under UV Radiation.

Nonmetallic ionic liquids (ILs) exhibit unique advantages in catalyzing poly (ethylene terephthalate) (PET) glycolysis, but usually require longer reaction times. We found that exposure to UV radiation can accelerate the glycolysis reaction and significantly reduce the reaction time. In this work, we synthesized five nonmetallic dibasic ILs, and their glycolysis catalytic activity was investigated. 1,8-diazabicyclo [5,4,0] undec-7-ene imidazole ([HDBU]Im) exhibited better catalytic performance. Meanwhile, UV radiation is used as a reinforcement method to improve the PET glycolysis efficiency. Under optimal conditions (5 g PET, 20 g ethylene glycol (EG), 0.25 g [HDBU]Im, 10,000 µW·cm-2 UV radiation reacted for 90 min at 185 °C), the PET conversion and BHET yield were 100% and 88.9%, respectively. Based on the UV-visible spectrum, it was found that UV radiation can activate the C=O in PET. Hence, the incorporation of UV radiation can considerably diminish the activation energy of the reaction, shortening the reaction time of PET degradation. Finally, a possible reaction mechanism of [HDBU]Im-catalyzed PET glycolysis under UV radiation was proposed.

Analysing micro- and nanoplastics with cutting-edge infrared spectroscopy techniques: a critical review.

The escalating prominence of micro- and nanoplastics (MNPs) as emerging anthropogenic pollutants has sparked widespread scientific and public interest. These minuscule particles pervade the global environment, permeating drinking water and food sources, prompting concerns regarding their environmental impacts and potential risks to human health. In recent years, the field of MNP research has witnessed the development and application of cutting-edge infrared (IR) spectroscopic instruments. This review focuses on the recent application of advanced IR spectroscopic techniques and relevant instrumentation to analyse MNPs. A comprehensive literature search was conducted, encompassing articles published within the past three years. The findings revealed that Fourier transform infrared (FTIR) spectroscopy stands as the most used technique, with focal plane array FTIR (FPA-FTIR) representing the cutting edge in FTIR spectroscopy. The second most popular technique is quantum cascade laser infrared (QCL-IR) spectroscopy, which has facilitated rapid analysis of plastic particles. Following closely is optical photothermal infrared (O-PTIR) spectroscopy, which can furnish submicron spatial resolution. Subsequently, there is atomic force microscopy-based infrared (AFM-IR) spectroscopy, which has made it feasible to analyse MNPs at the nanoscale level. The most advanced IR instruments identified in articles covered in this review were compared. Comparison metrics encompass substrates/filters, data quality, spatial resolution, data acquisition speed, data processing and cost. The limitations of these IR instruments were identified, and recommendations to address these limitations were proposed. The findings of this review offer valuable guidance to MNP researchers in selecting suitable instrumentation for their research experiments, thereby facilitating advancements in research aimed at enhancing our understanding of the environmental and human health risks associated with MNPs.

Baking releases microplastics from polyethylene terephthalate bakeware as detected by optical photothermal infrared and quantum cascade laser infrared.

The use of plastic bakeware is a potential source of human exposure to microplastics (MPs). However, characterizing MPs remains a challenge. This study aims to employ optical photothermal infrared (O-PTIR) and quantum cascade laser infrared (QCL-IR) technology to characterise polyethylene terephthalate (PET) MPs shed from PET bakeware during the baking process. The bakeware, filled with ultrapure water, underwent baking cycles at 220 °C for 20 min, 60 min, and three consecutive cycles of 60 min each. Subsequently, particles present in the ultrapure water were collected using an Al2O3 filter. O-PTIR and QCL-IR were used to characterise PET MPs collected from the filtration. Analysis revealed that QCL-IR spectra exhibited broader absorption peaks, compared to O-PTIR. Notably, MP spectra obtained from both techniques displayed common absorption peaks around 1119, 1623, 1341 and 1725 cm-1. The dominant size of PET MPs detected by O-PTIR and QCL-IR was 1-3 µm and 5-20 µm, respectively. The quantity of identified PET MPs using O-PTIR was 18 times greater than that with QCL-IR, which was attributed to variations in spatial resolution, sampling methods for spectra collection, and data analysis employed by the two methods. Importantly, findings from both techniques highlighted a notably large quantity of MPs released from PET bakeware, particularly evident after 3 cycles of 60 min of baking, suggesting a substantial increase in the potential ingestion of MPs, especially in scenarios involving extended baking durations. The research outcomes will guide consumers on minimizing the intake of microplastics by using PET bakeware for shorter baking time. Additionally, the study will yield valuable insights into the application of O-PTIR and QCL-IR for MPs detection, potentially inspiring advancements in MPs detection methodologies through cutting-edge technologies.

Assessing the inconsistency of microplastic measurements in foods and beverages.

The widespread occurrence of microplastics (MPs) in the food chain has gained substantial recognition as a pressing concern, highlighting the inevitability of human exposure through ingestion of foodborne MPs, coupled with the release of MPs from plastic packaging. However, there are notable disparities in the reported numbers of MPs in foods and beverages, warranting a thorough investigation into the factors contributing to these discrepancies. Table salt is one of the major sources of MPs, and there was an approximately hundred-fold difference between the reviewed studies that reported the highest and lowest number of MPs. In addition, more noticeable discrepancies were discovered between studies on MPs released from teabags. One study reported that approximately 15 billion MPs were released into a cup of tea from a single teabag, whereas another research paper found only approximately 106.3 ± 14.6 MP/teabag after brewing. This comprehensive review focuses on the inconsistencies observed across studies examining MPs, shedding light on the plausible factors underlying these variations. Furthermore, the review outlines areas in analytical procedures that require enhancement and offers recommendations to promote accuracy and standardization in future research efforts, such as employing analytical methods capable of confirming the presence of MPs, using appropriate filter sizes, considering representative sample sizes when extrapolation is involved, and so on. By pinpointing the detection processes leading to the inconsistent results observed in MP studies, this comparative analysis will contribute to the development of reliable analytic methods for understanding the extent of microplastic contamination in the human food chain.

Machine learning driven methodology for enhanced nylon microplastic detection and characterization.

In recent years, the field of microplastic (MP) research has evolved significantly; however, the lack of a standardized detection methodology has led to incomparability across studies. Addressing this gap, our current study innovates a reliable MP detection system that synergizes sample processing, machine learning, and optical photothermal infrared (O-PTIR) spectroscopy. This approach includes examining high-temperature filtration and alcohol treatment for reducing non-MP particles and utilizing a support vector machine (SVM) classifier focused on key wavenumbers that could discriminate between nylon MPs and non-nylon MPs (1077, 1541, 1635, 1711 cm-1 were selected based on the feature importance of SVM-Full wavenumber model) for enhanced MP identification. The SVM model built from key wavenumbers demonstrates a high accuracy rate of 91.33%. Results show that alcohol treatment is effective in minimizing non-MP particles, while filtration at 70 °C has limited impact. Additionally, this method was applied to assess MPs released from commercial nylon teabags, revealing an average release of 106 particles per teabag. This research integrates machine learning with O-PTIR spectroscopy, paving the way for potential standardization in MP detection methodologies and providing vital insights into their environmental and health implications.

The ionic liquids upon perchlorate to promote the C-C/C-O bonds cleavage in alkali lignin under photothermal synergism.

Transforming lignin into aromatic monomers is critically attractive to develop green and sustainable energy supplies. However, the usage of the additional catalysts like metal or base/acid is commonly limited by the caused repolymerized and environmental issues. The key step is to mediate electron transfer in lignin to trigger lignin C-C/C-O bonds cleavage without the catalysts mentioned above. Here, we report that the ionic liquids [BMim][ClO4] was found to trigger lignin electron transfer to cleave the C-C/C-O bonds for aromatic monomers without any additional catalyst. The proton transfer from [BMim]+ to [ClO4]- could polarize the anion and decrease its structure stability, upon which the active hydroxyl radical generated and induced lignin C-C/C-O bonds fragmentation via free radical-mediated routes with the assistance of photothermal synergism. About 4.4 wt% yields of aromatic monomers, mainly composed of vanillin and acetosyringone, are afforded in [BMim][ClO4] under UV-light irradiation in the air at 80 °C. This work opens the way to produce value-added aromatic monomers from lignin using an eco-friendly, energy-efficient, and simple route that may contribute to the sustainable utilization of renewable natural resources.

Real-Time Detection of Strawberry Ripeness Using Augmented Reality and Deep Learning.

Currently, strawberry harvesting relies heavily on human labour and subjective assessments of ripeness, resulting in inconsistent post-harvest quality. Therefore, the aim of this work is to automate this process and provide a more accurate and efficient way of assessing ripeness. We explored a unique combination of YOLOv7 object detection and augmented reality technology to detect and visualise the ripeness of strawberries. Our results showed that the proposed YOLOv7 object detection model, which employed transfer learning, fine-tuning and multi-scale training, accurately identified the level of ripeness of each strawberry with an mAP of 0.89 and an F1 score of 0.92. The tiny models have an average detection time of 18 ms per frame at a resolution of 1280 × 720 using a high-performance computer, thereby enabling real-time detection in the field. Our findings distinctly establish the superior performance of YOLOv7 when compared to other cutting-edge methodologies. We also suggest using Microsoft HoloLens 2 to overlay predicted ripeness labels onto each strawberry in the real world, providing a visual representation of the ripeness level. Despite some challenges, this work highlights the potential of augmented reality to assist farmers in harvesting support, which could have significant implications for current agricultural practices.

Early changes of ganglion cell-inner plexiform layer thickness and macular microvasculature in Posner-Schlossman syndrome: a binocular control study by OCTA.

To evaluate the early changes in ganglion cell-inner plexiform layer thickness and macular microvasculature in Posner-Schlossman syndrome (PSS) with a binocular control study involving optical coherence tomography angiography (OCTA). Twenty-six patients with unilateral PSS were included in this cross-sectional study. All subjects underwent a thorough ocular examination. Macular ganglion cell-inner plexiform layer (mGCIPL) and superficial macular microvasculature measurements, including vessel density (VD), perfusion density (PD) and the foveal avascular zone (FAZ), were recorded. In PSS-affected eyes, the mGCIPL thickness was significantly lower in all quadrants than in the contralateral eyes (all p < 0.05). Significant macular microvascular damage was found in the PSS-affected eyes, including whole-image VD (wiVD), wiPD, perifoveal VD (periVD) and periPD (all p < 0.05); but there was no obvious difference in parafoveal VD (paraVD), paraPD and FAZ parameters (all p > 0.05). In addition, a decreased wiVD and wiPD were significantly correlated with a smaller mGCIPL thickness and a decreased MD (all p < 0.05). These parameters may contribute to the early detection of glaucomatous damage and timely supervision of disease progression in PSS.

Microplastics in waste management systems: A review of analytical methods, challenges and prospects.

Numerous studies have reported the presence of microplastics (MPs) in waste collection and disposal systems. However, current scientific studies on measuring MP occurrence in a waste management context are not comparable due to a lack of standardized methodologies. Consequently, the impact of MPs on ecosystems and human health remains largely unclear. To address the inconsistencies, present in published studies, this review thoroughly examines sample preparation techniques for transfer stations, landfill leachate, recycling, compost, and incineration ash samples. Furthermore, various analytical approaches such as flotation, filtration, and organic matter digestion, as well as morphological categorization, identification, and quantification, are subsequently rigorously assessed. The benefits and limitations of each methodology are evaluated to facilitate the development of accurate and effective methods for detecting and characterizing nanoplastics. Recent research suggests that plastic recycling and composting facilities are the primary environmental sources of microplastic pollution among different waste treatment methods. The most prevalent microplastic types discovered in waste management were polyethylene (PE) and polypropylene (PP), with fragment and fiber being the most frequently reported morphologies. The review highlights a number of tactics that could be integrated into the methodology development for detecting microplastics in waste management systems (WMS), ultimately leading to better consistency and reliability of data across different studies. In essence, this will advance our comprehension of potential risks associated with microplastics.

Mechanism of dielectric barrier plasma technology to improve the quantity and quality of short chain fatty acids in anaerobic fermentation of cyanobacteria.

The recycling of high value carbon resources from cyanobacteria has become a research hotspot. This work investigated the possibility of dielectric barrier discharge (DBD) plasma pretreatment to improve the anaerobic fermentation performance of cyanobacteria. The maximum accumulations of short-chain fatty acids (SCFAs) and acetic acid in DBD group were 3.30 and 1.49 times of that in control group. The physical effects of DBD plasma and the oxidative stress response of cyanobacteria cells could improve the solubilization of cyanobacteria polymer. The destruction of humus by DBD plasma can reduce the negative impact of humus on the early stage of anaerobic fermentation, thus facilitating the rapid start of anaerobic fermentation. The contents of Bacteroidetes, Firmicutes and Chloroflexi in DBD group were higher than those in control group, while the content of Proteobacteria was on the contrary, which was conducive to the hydrolysis and acidification process. The decrease of Methanosaeta sp. and Methanosarcina sp. abundance in DBD group might be another reason for the increase of acetic acid ratio. Under the joint action of plasma chemical oxidation and microbial degradation, the degradation effect of microcystin-LR in the anaerobic fermentation supernatant of DBD group was better than that of the control group, which was conducive to the recycling of cyanobacteria anaerobic fermentation supernatant. Therefore, DBD pretreatment was conductive to recycling valuable carbon source from cyanobacteria and can be further developed as a potential new pretreatment technology.

Trends of Augmented Reality for Agri-Food Applications.

Recent years have witnessed an increasing interest in deploying state-of-the-art augmented reality (AR) head-mounted displays (HMDs) for agri-food applications. The benefits of AR HMDs to agri-food industry stakeholders (e.g., food suppliers, retail/food service) have received growing attention and recognition. AR HMDs enable users to make healthier dietary choices, experience novel changes in their perception of taste, enhance the cooking and food shopping experience, improve productivity at work and enhance the implementation of precision farming. Therefore, although development costs are still high, the case for integration of AR in food chains appears to be compelling. This review will present the most recent developments of AR HMDs for agri-food relevant applications. The summarized applications can be clustered into different themes: (1) dietary and food nutrition assessment; (2) food sensory science; (3) changing the eating environment; (4) retail food chain applications; (5) enhancing the cooking experience; (6) food-related training and learning; and (7) food production and precision farming. Limitations of current practices will be highlighted, along with some proposed applications.

Population genomics of emerging Elizabethkingia anophelis pathogens reveals potential outbreak and rapid global dissemination.

Elizabethkingia anophelis is an emerging species and has increasingly been reported to cause life-threatening infections and even outbreaks in humans. Nevertheless, there is little data regarding the E. anophelis geographical distribution, phylogenetic structure, and transmission across the globe, especially in Asia. We utilize whole-genome sequencing (WGS) data to define a global population framework, phylogenetic structure, geographical distribution, and transmission evaluation of E. anophelis pathogens. The geographical distribution diagram revealed the emerging pathogenic bacteria already distributed in various countries worldwide, especially in the USA and China. Strikingly, phylogenetic analysis showed a part of our China original E. anophelis shared the same ancestor with the USA outbreak strain, which implies the possibility of localized outbreaks and global spread. These closer related strains also contained ICEEaI, which might insert into a disrupted DNA repair mutY gene and made the strain more liable to mutation and outbreak infection. BEAST analysis showed that the most recent common ancestor for ICEEaI E. anophelis was dated twelve years ago, and China might be the most likely recent source of this bacteria. Our study sheds light on the potential possibility of E. anophelis causing the large-scale outbreak and rapid global dissemination. Continued genomic surveillance of the dynamics of E. anophelis populations will generate further knowledge for optimizing future prevent global outbreak infections.

Exploring the identification of multiple bacteria on stainless steel using multi-scale spectral imaging from microscopic to macroscopic.

This work investigates non-contact reflectance spectral imaging techniques, i.e. microscopic Fourier transform infrared (FTIR) imaging, macroscopic visible-near infrared (VNIR), and shortwave infrared (SWIR) spectral imaging, for the identification of bacteria on stainless steel. Spectral images of two Gram-positive (GP) bacteria (Bacillus subtilis (BS) and Lactobacillus plantarum (LP)), and three Gram-negative (GN) bacteria (Escherichia coli (EC), Cronobacter sakazakii (CS), and Pseudomonas fluorescens (PF)), were collected from dried suspensions of bacterial cells dropped onto stainless steel surfaces. Through the use of multiple independent biological replicates for model validation and testing, FTIR reflectance spectral imaging was found to provide excellent GP/GN classification accuracy (> 96%), while the fused VNIR-SWIR data yielded classification accuracy exceeding 80% when applied to the independent test sets. However, classification within gram type was far less reliable, with lower accuracies for classification within the GP (< 75%) and GN (≤ 51%) species when calibration models were applied to the independent test sets, underlining the importance of independent model validation when dealing with samples of high biological variability.

A review of potential human health impacts of micro- and nanoplastics exposure.

This systematic review aims to summarize the current knowledge on biological effects of micro- and nanoplastics (MNPs) on human health based on mammalian systems. An extensive search of the literature led to a total of 133 primary research articles on the health relevance of MNPs. Our findings revealed that although the study of MNP cytotoxicity and inflammatory response represents a major research theme, most studies (105 articles) focused on the effects of polystyrene MNPs due to their wide availability as a well characterised research material that can be manufactured with a large range of particle sizes, fluorescence labelling as well as various surface modifications. Among the 133 studies covered in this review, 117 articles reported adverse health effects after being exposed to MNPs. Mammalian in vitro studies identified multiple biological effects including cytotoxicity, oxidative stress, inflammatory response, genotoxicity, embryotoxicity, hepatotoxicity, neurotoxicity, renal toxicity and even carcinogenicity, while rodent in vivo models confirmed the bioaccumulation of MNPs in the liver, spleen, kidney, brain, lung and gut, presenting adverse effects at different levels including reproductive toxic effects and trans-generational toxicity. In contrast, the remaining 16 studies indicated an insignificant impact of MNPs on humans. A few studies attempted to investigate the mechanisms or factors driving the toxicity of MNPs and identified several determining factors including size, concentration, shape, surface charge, attached pollutants and weathering process, which, however, were not benchmarked or considered by most studies. This review demonstrates that there are still many inconsistencies in the evaluation of the potential health effects of MNPs due to the lack of comparability between studies. Current limitations hindering the attainment of reproducible conclusions as well as recommendations for future research directions are also presented.

META-PLS modelling: An integrated approach to automatic model optimization for near-infrared spectra.

Predictive latent space near-infrared (NIR) spectral modelling with PLS (Partial Least Squares) has two main tasks that require user input to achieve optimal models. The first is the selection of the optimal pre-processing of NIR spectra and the second is the selection of the optimal number of PLS model components assuming the data is outlier free. Often the two tasks are performed in an exhaustive search to find the best pre-processing as well as the optimal number of model components. We propose a novel approach called meta partial least square (META-PLS) which drops the need for both the pre-processing optimisation and exhaustive search for optimal model components. We utilise the stepwise nature of the PLS algorithm to learn complementary information from different pre-processed forms of the same data set as performed in multiblock pre-processing ensemble models to avoid pre-processing selection but receive help from the pre-processing ensembles, and deploy a weighted randomisation test to decide the optimal number of model components automatically. The performance of the approach for performing automatic NIR spectral modelling is demonstrated with several real data sets.

Mechanism of dielectric barrier discharge plasma technology to improve the quantity of short-chain fatty acids in anaerobic fermentation of waste active sludge.

The mechanism of improving the anaerobic fermentation performance of waste active sludge by using dielectric barrier discharge (DBD) plasma pretreatment technology was investigated. The maximum accumulation of short-chain fatty acids (SCFAs) was observed on the 7th day of anaerobic fermentation when the DBD power was 76.50 W, which was 1726.70 mg COD/L, 1.50 times of the control group. The ratio of acetic acid in DBD group was 9.30% higher than that in the control. Further mechanism research indicated that DBD pretreatment can destroy the structure of extracellular polymer substances and release organic substances such as protein and polysaccharide. The dissolved organic matter analysis indicated that the DBD technique could increase the release of biodegradable organics (eg., tyrosine proteins, soluble microbial by-products), thus accelerate the biotransformation of organic substance. Bacterial community structure analysis showed that the increase in the abundance of Firmicutes and Bacteroidetes and the decrease in the abundance of Proteobacteria in DBD group were beneficial to the accumulation of SCFAs. Besides, further archaeal analysis indicated that the decrease of Methanosaeta sp. and Methanosarcina sp. abundance in the DBD group facilitate acetic acid accumulation. This study demonstrated that the DBD technique can be used as an effective and potential pretreatment method to improve sludge anaerobic fermentation performance.

[Provenance of Groundwater Solute and Its Controlling Factors in Yancheng Area].

Affected by human activities and climate change, the deterioration of groundwater quality could continue to intensify, and it is difficult to repair after being polluted. In order to reduce the vulnerability of groundwater safety, it is urgent to strengthen research on groundwater quality. To analyze the characteristics of hydrochemical composition and control factors and to explore the impact of human activities, groundwater sampling was conducted in the alluvial and marine plains of Yancheng area, where industrial and agricultural activities were intense. The results showed that:① the TDS value of shallow groundwater was between 211 and 3790 mg·L-1, of which, brackish water samples accounted for 30.28% in Yancheng. The order of the concentration of anions and cations was:HCO3->Cl->SO42->NO3- and Na+>Ca2+>Mg2+>K+. However, from Type Ⅰ/Ⅱ water to Type Ⅴ water, with the increase in TDS value, the dominant anions and cations evolved from HCO3 type to Cl/SO4 type and from Ca type to Na type, respectively. ② In the initial Type Ⅰ/Ⅱ water bodies, Na+ should have mainly originated from the weathering erosion of albite, whereas Ca2+, Mg2+, and HCO3- should have had a common rock weathering source. In the subsequent water quality evolution process, the factors that led the TDS value to increase, or even to exceed the standard, were not limited to rock weathering, and the effects of evaporation-concentration and ion exchange were also very worthy of attention. ③ The enrichment factor analysis showed that the Cl- and SO42- were mainly from the input of sea salt sources, and their proportions were gradually increasing from the freshwater areas of Type Ⅰ/Ⅱ in the west to the brackish water areas in the east. However, Ca2+, K+, and HCO3- were mainly derived from crustal sources, and Ca2+ was precipitated due to saturation. ④ The NO3- derived from agricultural fertilizers was limited to water of category Ⅰ/Ⅱ and Ⅲ-1 (500 mg·L-1 < TDS ≤ 750 mg·L-1). Discharge of urban sewage mainly affected Type Ⅲ water bodies. The nitrates formed by agricultural organic fertilizers were mainly distributed in the water of Type Ⅳ and Ⅴ, which was the main rice-producing area of Sheyang, Jiangsu, and the SO42- formed by human activities in this area could be ignored. These should be one of the achievements in the development of regional ecological agriculture.

Combining deep learning with chemometrics when it is really needed: A case of real time object detection and spectral model application for spectral image processing.

Deep learning (DL) being popularly used in computer vision applications is still in its early stage in chemometric domain for spectral image processing. Often the challenge is that there are too few samples from analytical laboratory experiments to preform DL. In this study, we present a novel combination of DL and chemometrics to process spectral images even with as few as < 100 spectral images. We divided the image processing part such as object detection and recognition as the DL task and prediction of chemical property as the chemometric task based on latent space modelling. For image processing tasks of object detection and recognition, transfer learning was performed on the pretrained YOLOv4 object detection network weights to adapt the model to work well on spectral images captured in laboratory settings. Once the object is identified with DL, a background query is performed for the pre-built chemometric models to select the model for predicting the properties for specific object. The obtained results showed good potential of using DL and chemometric approaches in conjunction to reap the best of both scientific domains. This approach is of high interest to whoever involved in spectral imaging and dealing with object detection and physicochemical properties prediction of the samples with chemometric approaches.