Adulteration of cosmetic products with toxicants remains a public-health concern, especially in markets where surveillance is uneven. We developed a non-destructive screening method for mercury and salicylic acid using hyperspectral imaging (400–1000 nm, Specim FX10 camera) coupled with machine-learning (ML) chemometrics. A total of 1,800 hyperspectral image samples (covering nine classes, including pure and graded adulteration) were collected. Since each hyperspectral image produces a large volume of spectral information, preprocessing was applied to extract usable data. This process resulted in approximately 45,000 images spanning 224 spectral bands/features, which were then used for machine learning–based predictive analysis. The spectra were rigorously preprocessed using the empirical line method for radiometric calibration and Savitzky–Golay smoothing before comparative modelling with histogram-based gradient boosting (H-GB), artificial neural networks (ANN), one-dimensional convolutional neural networks (1D-CNN), and one-vs-one linear discriminant analysis (OvO-LDA). To the best of our knowledge, this is the first application of HSI integrated with a 1D-CNN for cosmetic adulterant screening, highlighting its novelty within this domain. The 1D-CNN delivered the best overall performance, with validation accuracy up to 97% (10-fold CV mean 0.940 ± 0.015), while OvO-LDA was the most stable across folds; H-GB showed lower performance for low-level mercury adulteration. Spectral analysis highlighted 500–750 nm as the most discriminative region for both analytes, informing future band selection. The approach enables rapid, real-time screening without destroying samples, supporting regulatory spot-checks and factory-floor QC.
Phenotyping extensive populations remains a major constraint in tree breeding programmes, particularly due to the time-consuming and labour-intensive nature of conventional methods. Near infrared (NIR) spectroscopy, which is a high-throughput phenotyping method, offers an alternative solution, providing a rapid and cost-effective approach for assessing growth- and function-based traits on large numbers of trees. This study aimed to evaluate the potential of NIR spectroscopy-based models for predicting such traits in Larch. Specifically, delta carbon-13 ( δ 13 C), carbon (C), nitrogen (N), specific leaf area (SLA), leaf dry matter content (LDM), and phenolics on needles; the branch hydraulic trait (P 50 ), and lignin and hydroxyphenyl/guaiacyl (H/G) ratio on wood cores from an experimental study on Larix species were predicted using multivariate modelling, specifically, partial least squares regression. Reliable models were obtained for N content (R 2 training = 0.95, r 2 testing = 0.94), lignin (R 2 training = 0.95, r 2 testing = 0.94), and H/G ratio (R 2 training = 0.88, r 2 testing = 0.89), while moderate predictive performance was observed for C content (R 2 training = 0.79, r 2 testing = 0.79) and δ 13 C (R 2 training = 0.76, r 2 testing = 0.69). This methodological approach and its results encourage the transition from traditional laboratory methods to efficient, large-scale-based trait evaluation techniques in forestry.
Natural rubber is a vital industrial raw material and strategic resource. Ribbed smoked sheet is a traditional solid natural rubber that accounts for a significant proportion of the manufacturing of high-performance tyres. Presently, the grading of ribbed smoked sheet is primarily accomplished through manual labor, with considerable influence stemming from subjective factors. Accurate and objective differentiation of ribbed smoked sheet grades is imperative for subsequent production and processing. This study used a handheld near infrared (NIR) spectrometer to collect spectral data on natural and ribbed smoked sheet across two main categories and four grades. A classification model for ribbed smoked sheet was then established using a convolutional neural network (CNN) algorithm and compared with traditional methods, such as the multilayer perceptron and random forest algorithms. The results showed that the CNN-based classification model for ribbed smoked sheets had the highest accuracy on the test set at 98.33%, and the Area Under the Receiver Operating Characteristic (ROC) curve (AUC) for each category exceeds 0.99, demonstrating the best overall performance. The model was further validated using an independent external sample set, achieving an accuracy of 80%. The classification model developed using a handheld near infrared spectrometer and convolutional neural network algorithms offers a new technical approach to grading ribbed smoked sheet.
Rapid determination of the physico-chemical properties of diesel is of significant importance for both refinery production and environmental protection. To address the time-consuming nature of traditional chemical analysis and the tendency of back propagation neural networks (BPNN) to converge into local optima, this study proposes a near infrared (NIR) spectroscopic method for determining diesel properties by integrating chemometrics with a genetic algorithm-particle swarm optimization BPNN (GA-PSO-BP). The diesel spectral data were preprocessed using Savitzky-Golay (S-G) smoothing and differentiation, while outliers were identified through Monte Carlo cross-validation (MCCV). The boosting soft shrinkage (BOSS) algorithm was employed to extract key characteristic wavelengths from the high-dimensional spectra, significantly compressing the feature dimensionality and reducing model complexity. Finally, a hybrid GA-PSO algorithm was utilized to optimize the initial parameters of the BPNN, effectively overcoming its inherent limitations regarding local optima. The results demonstrate that the GA-PSO-BP model outperforms traditional machine learning models in predicting six diesel properties, with the coefficient of determination (R 2 ) reaching maximum values across all parameters. Furthermore, SHAP (SHapley Additive exPlanations) analysis was introduced to reveal the nonlinear mapping relationship between spectral absorbance and physicochemical indices, thereby enhancing model interpretability. This research provides an efficient and robust solution for the rapid and non-destructive monitoring of diesel quality.
The growing global demand for fresh fruits necessitates efficient, non-destructive methods for assessing fruit quality, especially for export. Traditional fruit quality assessment techniques are often labor-intensive, time consuming, and destructive, making them unsuitable for large-scale or real-time analysis. To address these limitations, this study provides a comprehensive bibliometric analysis of near infrared spectroscopy (NIR spectroscopy) in fruit analysis. The study adhered to the PRISMA guidelines to extract peer-reviewed papers from 2003 to 2023 from the Scopus database. Thereafter, the bibliometric analysis was conducted using R software’s Bibliometrix package to evaluate global trends, key contributors, and emerging themes in the field. The results show that NIR spectroscopy has become an essential tool for non-destructive quality assessment in fruits, accurately predicting attributes such as total acidity, soluble solids content, and internal disorders. Integrating machine learning and artificial intelligence models, particularly artificial neural networks and deep learning, has further enhanced the predictive capabilities of NIR spectroscopy. In addition, technological innovations such as portable spectrometers and hyperspectral imaging have expanded the applicability of NIR spectroscopy beyond laboratory settings to in-field assessments. The findings highlight the ongoing evolution of NIR spectroscopy technology, its significant impact on fruit quality evaluation, and the potential for future advancements in this field. Future research should focus on improving the adaptability of NIR spectroscopy to diverse fruit types and production environments and exploring the use of artificial intelligence and machine learning to further enhance data interpretation and predictive accuracy. Such innovations could significantly broaden the scope of NIR spectroscopy applications, making it a critical tool for sustainable agriculture and global food security.
As milk adulteration and contamination continue to raise a serious threat to public health, there is a growing demand for rapid, reliable, and non-destructive techniques for milk quality assessment in the dairy industry. This comprehensive review provides recent advancements in non-invasive spectroscopic approaches, with a particular focus on near infrared (NIR) spectroscopy and visible-NIR hyperspectral imaging (Vis-NIR-HSI) for detecting both adulterants and contaminants in milk. Combined with chemometrics and machine learning techniques, these approaches can be used as effective qualitative and quantitative methods. The complete analytical workflow is examined in-depth with the significance of preprocessing strategies on spectral quality, the requirements of wavelength selection, and the roles of targeted and non-targeted chemometric models on classification and regression tasks. Finally, this review addresses existing limitations, including insufficient databases and the complexities of multi-adulterant detection, as well as highlights the emerging trends as future opportunities in multimodal data fusion, Internet of Things (IoT), and deep learning to support real-time monitoring for milk. The purpose of this review is to facilitate the practical, large-scale adoption of robust, non-destructive NIR and Vis-NIR-HSI tools for milk authentication.
Spectroscopy provides a new proxy for reconstructing prehistoric raw-material procurement, mobility, and inter-settlement interaction, through linking archaeological stone tools to specific quarry sources. In this paper, spectral responses have been analyzed using chemometric methods, particularly principal component analysis (PCA) and t-distribution stochastic neighbour embedding (t-SNE), to capture meaningful differences between quartz and quartzite raw materials and to analyse possible relationships between archaeological stone tool assemblages and quarries. Field adapted near infrared reflectance (NIR) spectroscopy and X-ray fluorescence (XRF) instruments have been used to analyse prehistoric quartz and quartzite quarries in inland V & auml;sterbotten (Sweden), and the collected spectral data were compared with tool assemblages from curated archaeological collections. Despite challenges inherent to field sampling, the combined spectroscopic approach reliably differentiates raw material groups, demonstrating its suitability for archaeological prospection and excavation. These findings underscore the value of integrating spectroscopy into routine fieldwork, providing a modern analytical toolkit that expands the interpretive potential of archaeological fieldwork.
Clover (Trifolium) plants with rich protein, fiber and nutrient contents provide high forage yield, essential for efficient animal production. They also offer further ecological benefits including erosion control, soil reclamation, pollinator support, and urban greening. Wild clover species possess vital genetic traits to adapt to adverse growing conditions including drought, heat stress, and diseases. Thus, precise and quick identification of clover species can significantly aid successful plant breeding and sustainable agriculture. Human-based evaluations may not always be objective, while laboratory genetic analyses require significant amount of time, labor, and cost. The present work is the first study to accurately classify wild Trifolium species by using near infrared (NIR) reflectance spectroscopy and machine learning (ML) algorithms. NIR reflectance data (4000-10,000 cm-1) of 146 dried-ground plant samples belonging to nine Trifolium species were used for the classification along with four data pretreatment methods and six ML algorithms. Three different data sets were utilized in the analysis: (a) the data with no dimensional reduction, (b) the data with dimensional reduction by using principal component analysis (PCA), and (c) the data with dimensional reduction by using linear discriminant analysis (LDA). The most successful result was obtained with the LDA data coupled with multiplicative scatter correction (MSC) and K-nearest neighbors (KNN) algorithm with 98% test classification accuracy. The results of this study showed that the NIR spectroscopy coupled with ML algorithms can be utilized to correctly identify the Trifolium species needed for effective plant breeding and conservation strategies.
This study reports a novel process analytical methodology for monitoring thermal co-amorphization of lidocaine (LID) and indomethacin (IMC) by combining in situ near-infrared (NIR) spectroscopy, terahertz (THz) spectroscopy, and chemometric analysis. The novelty of this work lies in the real-time, in situ monitoring of molecular-level structural reorganization during thermal co-amorphization, enabling identification of transient intermediate states that have not been previously reported. Unlike conventional approaches that characterize co-amorphous systems only at fixed end-point states, we continuously monitored molecular-level structural reorganization during heating from 30 degrees C to 80 degrees C in real time, enabling identification of transient intermediate states and phase separation behavior. The co-amorphous LID-IMC materials were synthesized by thermal melt synthesis at 80 degrees C, as indicated by broad powder X-ray diffraction patterns. In situ NIR spectral measurements during heating were analyzed using machine learning techniques, revealing that the mixture underwent separation into two distinct components with generation of intermediate states during co-amorphous formation. FTIR spectroscopy indicated the dissociation of indomethacin dimers and their subsequent binding with lidocaine. Terahertz spectroscopy revealed temperature-dependent structural reorganization, with distinct vibrational modes observed at cryogenic and room temperatures, including a thermally stable mode at 4.90 THz. This combined NIR-THz approach suggests a novel quality control methodology for co-amorphization processes in pharmaceutical applications, offering real-time, non-contact monitoring of structural transformations during production cycles.
To address the challenge of discriminating Baijiu base liquor grades using near infrared (NIR) spectroscopy caused by highly similar chemical compositions and severe spectral overlap, this study proposes a robust NIR spectroscopy analytical strategy based on dual-domain feature decoupling. Unlike traditional approaches that treat background variations as noise, the proposed method simultaneously decodes chemical absorption features and spectral morphological features from the same signal. Specifically, first-order Savitzky-Golay (SG) derivatives are employed to enhance narrow-band absorption peaks associated with functional groups such as C-H and O-H, while continuous wavelet transform (CWT) is utilized to extract global morphological variations related to scattering effects and baseline drift. To improve the robustness of high-dimensional feature selection, a stability-guided screening method based on dynamic reference features is introduced to effectively retain consistently informative variables. Experimental results demonstrate that this method significantly reduces feature dimensionality while boosting classification accuracy to 97.56% in cross-validation and 97.78% on an independent test set. Further analysis indicates that spectral morphological features provide complementary discriminatory evidence to chemical absorption information, thereby enhancing model robustness under complex matrix conditions. This study provides a practical reference strategy for the grading of Baijiu base liquor.
Deploying a calibration based on near-infrared NIR spectroscopy in a factory setting often includes first creating a calibration based on laboratory data and then a calibration based on data from the factory. This has been done in this work, where the aim was to establish in-line monitoring of fat content in drycured sausages directly after the sausage stuffer. The laboratory data was also used to create an industrial calibration, with factory data used for tuning and testing. This approach has the potential to reduce the required factory samples in similar scenarios if the performance of a model based on laboratory data is sufficient. The root mean square error of prediction of the final factory validation set was 0.9% for the laboratory model and 0.8% for the factory model. The calibrated NIR spectroscopy instrument was used in process to monitor the fat variation in the sausages over two periods of 2 days each. The in-line measurements revealed that there was 0.5% variation in fat content between different batches using the same raw materials, and 0.6% variation within batches. The strategy of deploying an in-line NIR spectroscopy instrument gives efficient insight into the process variation and clues about how it can be improved for better raw material utilization and more reliable end-product quality. In this case, there was a factory upgrade between the two measurement trials. The measurements showed that the factory upgrade resulted in a reduction of the standard deviation of the within-batch fat content from 0.60% to 0.32% (after taking the measurement error into account).
In the present study, a rapid, non-destructive and real-time detection of the adulteration in Arnebia euchroma (Royle) Johnst. (A. euchroma) samples were established using portable near infrared spectrometer in combination to chemometrics. 37 batches of A. euchroma, 3 batches of Onosma paniculatum Bur. et Franch (O. paniculatum), 8 batches of Arnebia benthamii (Wall. ex G. Don) Johnston (A. benthamii) samples were collected. Besides, the adulterated A. euchroma samples were prepared by mixing A. euchroma with O. paniculatum or A. benthamii in different ratios, respectively. Afterwards, the near infrared spectra of the samples were acquired by a portable near infrared spectrometer. Firstly, a classification model was established by using data driven-soft independent modeling by class analogy (DD-SIMCA) and partial least squares-discriminant analysis (PLS-DA) algorithms, respectively, for differentiating the A. euchroma from its adulterants. Secondly, a quantitative model for detecting the adulteration concentration in adulterated A. euchroma samples was constructed by applying PLS and support vector machine (SVM) algorithms, respectively. The classification models of DD-SIMCA and PLS-DA achieved 100% of accuracy in calibration sets, as well as an accuracy of 95.6% and 100%, respectively, in the prediction sets. Moreover, the PLS regression model registered a ratio of prediction to deviation (RPD) value and root mean square error of prediction (RMSEP) value of 2.2, 1.6 and 8.70%, 12.03%, respectively, for O. paniculatum and A. benthamii adulterants, while the SVM model yielded the RPD value and the RMSEP value of 8.2, 3.4 and 2.29%, 5.55%, respectively. In summary, portable near infrared spectrometer combined with chemometrics was verified to be capable of achieving rapid, real-time detecting of adulteration of A. euchroma.
Efficient plastic waste management relies on the accurate identification and sorting of materials, which are essential for advancing recycling and upcycling efforts. While laser-induced breakdown spectroscopy is commonly used for polymer detection, hyperspectral imaging (HSI) offers a non-destructive alternative with enhanced spectral resolution, enabling differentiation of visually similar materials. However, HSI's high-dimensional data poses challenges for real-time processing. This study aims to develop a real-time deep learning-based detection model inspired by YOLOv2 (You Only Look Once). The proposed model classifies and localizes four common polymer types, namely; polypropylene, polyethylene terephthalate, high-density polyethylene, and polystyrene in mixed plastic waste streams using HSI data (900-1700 nm). By utilizing the YOLOv2 framework to automatically learn and extract discriminative spectral-spatial features, the model effectively processes the spectral content of HSI data for rapid and accurate inference. Experimental results show a mean average precision of 94.5% at an Intersection over Union threshold of 0.50 and a processing time of 0.086 s per image, with an average of 23 polymers per image. Although YOLOv3 offered slightly higher accuracy, YOLOv2 was chosen for its superior speed-accuracy tradeoff, making it more suitable for real-time industrial applications. These findings demonstrate that integrating hyperspectral imaging with lightweight deep learning models can deliver scalable, high-speed, and high-accuracy solutions for plastic waste sorting-potentially enhancing recycling efficiency and supporting circular economy goals.
Conventional multiplicative scatter correction (MSC) assumes that inter-sample variability can be modelled as a uniform multiplicative effect across the full spectrum, a constraint that limits its effectiveness for broad ultraviolet-visible and near infrared (UV-Vis-NIR) spectroscopy of heterogeneous plant materials. In sugarcane leaves, where localized spectral distortions arise from variable pigment distributions and tissue structures, such a global approach can leave residual baseline and slope artifacts, reducing predictive accuracy. As a proposed alternative, this study addresses this limitation by comparing and evaluating two localized MSC variants, namely Piecewise MSC (PMSC) and Segmented MSC (SMSC), for total chlorophyll content estimation. UV-Vis-NIR reflectance spectra (200-1400 nm) were collected from 166 sugarcane leaf samples during tillering stages and the corresponding chlorophyll contents were measured. The MSC, PMSC and SMSC corrected spectra were used to establish partial least squares (PLS) regression models on both the full spectrum and reduced spectra derived from five established wavelength selection approaches. Experimental results showed that across all preprocessing-selection combinations, PMSC and SMSC consistently outperformed MSC. Particularly, the PLS regression model employing PMSC preprocessing in combination with competitive adaptive reweighted sampling wavelength selection achieved the highest predictive performance (R-cal(2) = 0.81, r(pred)(2) = 0.72, RMSEC = 0.15 mg g(-1), RMSEP = 0.17 mg g(-1)), whereas SMSC exhibited a modest accuracy reduction of 2-19% while achieving an approximately 28-fold decrease in computational time. These findings demonstrate that localized scatter-correction strategies can enhance predictive modelling accuracy, offering a robust alternative to conventional MSC for agronomic trait estimation.
Lupins (Lupinus angustifolius) are a type of pulse known for their high protein content, nutritional benefits and versatile uses in food. Lupins have a characteristic thick outer hull which may impact the development and application of NIR (near infrared) calibrations for assessing compositional traits. This study seeks to compare the predictive abilities of models based on wholegrain and ground grist for key quality traits, specifically protein and moisture content. To determine if the thick outer hull poses an obstacle for NIR light to penetrate and reflect from the seed, NIR spectroscopy of lupin samples was conducted for both wholegrain and ground grist forms. The same set of 120 samples was used to construct two prediction models (with a calibration set of 96 samples and an independent validation set of 24 samples). The results show that the predictions for moisture content were comparable between wholegrain and grist for the validation set, achieving r2 values of 0.99 for both models. The models for protein content exhibited a greater distinction between wholegrain and grist, yielding r2 values of 0.91 and 0.96, respectively. Overall, both sets of models exhibited high correlations with protein and moisture constituents and are indicative that calibrations developed on intact lupin hull using wholegrain NIR spectra can be used to predict protein content. Despite a decline in accuracy when compared with the grist results, the benefit of applying the wholegrain calibration outweighs the time taken to prepare the grist samples for NIR analysis.
The study aimed to test the usefulness of the Hyperspectral imaging (HSI) in the visible and near infrared (VNIR) to address temporal and spatial variations in surface soil microbial respiration during an incubation. We hypothesized that VNIR HSI could show the heterogeneity and different evolutions of soil respiration between thermal stress and control conditions. Two mesocosms (approximate to 1500 cm2) were incubated 28 days under controlled conditions. Prior to incubation, one mesocosm was heat-stressed (stress), while the other was not (control). HS images of mesocosms (pixel resolution 0.18 x 0.36 mm) were acquired at several incubation dates. To calibrate respiration measurement of pixels, 56 standard samples were built and used as a calibration subterfuge to represent all conditions that would occur at the mesocosm surface in terms of straw cover, soil moisture, incubation date and consequences of initial thermal stress. Images of standard samples were acquired at the same dates than mesocosm images, just after CO2 emitted by each standard sample (i.e. respiration) was measured. Partial least squares (PLS) regression was used to establish a cumulative respiration model based on the average spectra of each standard samples at each incubation date. The calibration and validation yielded R2 = 0.88 and 0.85, respectively. The prediction model of respiration was applied on each pixel of the mesocosms and yielded prediction maps. The maps showed hotspots of respiration around plant residues surrounded by large areas of low respiration. The approach did not only contrast pieces of straw versus the soil matrix, but also hotspots with different evolutions. The effect of thermal stress on soil respiration patterns was not noticeable on mesocosms but was noted on standard samples with high straw cover. Further development of VNIR HSI at proximal scale should be encouraged to investigate the spatio-temporal processes of biological activity after a disturbance.
Near infrared (NIR) spectroscopy has been used to determine total phosphorus (total-P), phytate, and phytate-bound phosphorus concentrations in grains and animal feeds. However, since more than 50% of the total-P in grains of most cultivated cereals occurs as phytate (myo-inositolhexakisphosphate), there is a strong linear correlation between total-P and phytate-bound-P. If near infrared spectroscopy is used to analyse cereal products simultaneously for both total-P and for phytate-bound-P there is a risk that the intercorrelation effects will be dominant in models for each. This paper presents NIR spectra of whole wheat grains from progeny of a normal and a low phytate parent genotype. For these progeny there was a weak correlation between the phytate-bound-P and total-P concentrations (r = 0.5) and no correlation with grain protein (r = 0.1). The spectra of grains with lower phytate-bound-P differed at wavelengths that aligned mainly with O-H, C-H and P-OH bonds and included 7124 cm-1 (1404 nm), 5882 cm-1 (1701 nm) and 5245 cm-1 (1907 nm). Loading weights for partial least squares regression models indicated that models for both total-P and phytate-bound-P utilized essentially the same wavelengths. This supports the hypothesis that NIR calibrations for total-P and phytate-bound-P in wheat and wheat-based feeds are inter-dependent. This may not be critical for the determination of P in grain of most current genotypes due to the strong linear correlation between total-P and phytate-bound-P. However, to ensure more accurate analysis of P in grains of cereal varieties that carry genes for low phytate NIR calibration models for total-P, phytate-bound-P and phytate should be updated.
Biofilms are a health and operating issue in various fields, particularly in drip irrigation systems based on reclaimed water. To ensure the proper functioning of drip irrigation systems and save water, biofouling needs to be detected and quantified in drippers. Few studies have investigated the use of near infrared (NIR) spectroscopy for biofilm monitoring, particularly in the context of drip irrigation systems. A major challenge lies in the strong influence of water on NIR spectra, which complicates biofilm detection and analysis. This study assessed polarization spectroscopy as a new approach to detect biofilms in the presence of water and to discriminate between biofilms and the embedded insoluble elements. Samples were probed with near infrared polarized light to collect single and multi-scattered light separately. Principal component analysis of the spectra in the 1100-1300 nm region was used to distinguish spectra associated with water alone and with biofilm samples in water. Biofilms of a thickness of less than 100 µm could be detected in the presence of water. Partial least squares with discriminant analysis (PLS-DA) was used to discriminate biofilms, biofilms with kaolinite, and biofilms with calcium carbonate (all in the presence of water). The method achieved a discrimination accuracy of 96.4%, demonstrating strong potential for application in cases of biofilm-induced composite clogging. Thus, polarized light spectroscopy in the near infrared region proves effective overcoming the presence of water for detecting and discriminating biofilms. This approach holds promise for application across various fields where biofilm formation presents a challenge.
The near infrared (NIR) region of the electromagnetic spectrum contains valuable information about vibrational overtone and combination modes. Additionally, fluorophores that emit in the NIR are desired because this spectral range falls into the tissue transparency window. However, NIR techniques are not as widespread as their counterparts in the visible range because detectors, microscopes and fluorophores are less available. This study presents a NIR multimodal, low footprint spectroscopy (NIR-MMS) setup. The setup excites NIR fluorescence using a LED, or broadband NIR light to capture absorption spectra in the range from 900 nm to 1650 nm with a fiber-coupled InGaAs spectrometer. The study demonstrate the measurement of NIR fluorescent materials and biosensors for the neurotransmitter dopamine. Additionally, the setup allows the measurement of both absorption and emission spectra almost simultaneously to assess chemical changes in NIR fluorescent carbon nanotubes. Overall, this small footprint setup provides quasi-simultaneous access to NIR fluorescence and absorption spectra with broad applications in biosensing.
The 5th Aquaphotomics International Conference marked the 20th anniversary of the founding of Aquaphotomics. It was held from May 17 to May 20 this year at Kobe University under the theme “The Way of Water & Light, The Path of Life” and it was simultaneously streamed online. The conference was conducted in a hybrid format, with both inperson and online participation. On the first day, a public lecture for the general audience was held, followed by the Aquaphotomics School and the Aquaphotomics Workshop. From the second day onward, the program began with an overview session and expanded into a wide range of topics across ten thematic sessions, featuring 46 oral presentations and 15 poster presentations. The presentations covered diverse fields including the applications of Aquaphotomics in agriculture, medicine, and cosmetics, as well as technical themes such as spectroscopy, instrumentation, and chemometrics. Moreover, the conference included sessions on quantum electrodynamics, which drew significant attention. In particular, lectures by physicists specializing in quantum physics and the physical properties of water prompted meaningful discussions with spectroscopy researchers, marking an important step toward the development of a new interdisciplinary research framework. The conference attracted 257 participants from 21 countries, including Japan, and is expected to further promote international and interdisciplinary collaboration, contributing to the advancement of Aquaphotomics, near– infrared spectroscopy and related scientific research.
