Wavelength Selection Research Articles

A Nondestructive Detection Method for the Muti-Quality Attributes of Oats Using Near-Infrared Spectroscopy

This study aimed to achieve a precise and non-destructive quantification of the amounts of total starch, protein, β-glucan, and fat in oats using near-infrared technology in conjunction with chemometrics methods. Eight preprocessing methods (SNV, MSC, Nor, DE, FD, SD, BC, SS) were employed to process the original spectra. Subsequently, the optimal PLS model was obtained by integrating feature wavelength selection algorithms (CARS, SPA, UVE, LAR). After SD-SPA, total starch reached its optimal state (Rp2 = 0.768, RMSEP = 2.057). Protein achieved the best result after MSC-CARS (Rp2 = 0.853, RMSEP = 1.142). β-glucan reached the optimal value after BC-SPA (Rp2 = 0.759, RMSEP = 0.315). Fat achieved the optimal state after SS-SPA (Rp2 = 0.903, RMSEP = 0.692). The research has shown the performance of the portable FT-NIR for a rapid and non-destructive quantification of nutritional components in oats, holding significant importance for quality control and quality assessment within the oat industry.

Using Multi-Sensor Data Fusion Techniques and Machine Learning Algorithms for Improving UAV-Based Yield Prediction of Oilseed Rape

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture and field remote sensing. We explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras. Genetic algorithm–partial least square was employed and evaluated for effective wavelength (EW) or vegetation index (VI) selection. Additionally, different machine learning algorithms, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and radial basis function neural network (RBFNN), were developed and compared. With multi-source data fusion by combining vegetation indices (color and narrow-band VIs), robust prediction models of yield in oilseed rape were built. The performance of prediction models using the combination of VIs (RBFNN: Rpre = 0.8143, RMSEP = 171.9 kg/hm2) from multiple sensors manifested better results than those using only narrow-band VIs (BPNN: Rpre = 0.7655, RMSEP = 188.3 kg/hm2) from a multispectral camera. The best models for yield prediction were found by applying BPNN (Rpre = 0.8114, RMSEP = 172.6 kg/hm2) built from optimal EWs and ELM (Rpre = 0.8118, RMSEP = 170.9 kg/hm2) using optimal VIs. Taken together, the findings conclusively illustrate the potential of UAV-based RGB and multispectral images for the timely and non-invasive prediction of oilseed rape yield. This study also highlights that a lightweight UAV equipped with dual-image-frame snapshot cameras holds promise as a valuable tool for high-throughput plant phenotyping and advanced breeding programs within the realm of precision agriculture.

Substantial improvement of InGaN/GaN visible-light polarization-induced self-depletion phototransistor by thermally oxidized Al2O3

Atomic layer deposited (ALD) Al2O3 acting as gate dielectric and surface passivation is widely adopted in power electronics but seldom used in optoelectronic fields for its sophisticated and expensive technology. Herein, a simple but efficient Al2O3 passivation is used in the fabrication of InGaN/GaN visible-light (VL) polarization-induced self-depletion field effect phototransistors (FEPTs), for suppressing the surface leakage and recombination. The Al2O3 layer obtained by thermal oxidation (TO) of 2-nm-thick thermally evaporated metal Al shows high electrical insulation and even better passivation effect than the ALD-Al2O3. As a result, the dark current of TO-Al2O3 passivated device decreases by about 2 orders of magnitudes; meanwhile, the photoresponse increases by about 65%. Under a weak VL illumination of 6.8 μW/cm2, the InGaN/GaN FEPT exhibits a large photo-to-dark current ratio of 3.1 × 108 and an ultrahigh shot-noise-limited detectivity of 1.9 × 1018 jones. In addition, the FEPTs exhibit a strong wavelength selectivity with a 600 nm/400 nm spectral response rejection ratio exceeding 5 × 105. All these performances show huge potential in emerging VL applications that are limited by the insufficiencies of current Si photodetectors.

Integrated sensor chip of a resonant cavity light emitter and photon detector for wearable optical medicine

This work presents an integrated chip of a resonant cavity light emitter and photon detector (RCLEPD) to address the requirements of wearable optical medical devices for compact size, high efficiency, and interference resistance sensors. The optical radiation pattern and light extraction efficiency of the resonant cavity light emitting diode (RCLED) as well as the optical absorption spectrum of the resonant cavity enhanced photon detector (RCEPD) are theoretically simulated. Additionally, the wavelength selectivity of the RCEPD absorption spectrum is analyzed. Material epitaxial growth of RCLEPD was performed using metal-organic chemical vapor deposition (MOCVD), and an integrated sensing chip with an area of 2 × 2 mm2 was fabricated. Experimental results demonstrate that RCLED achieves a maximum external quantum efficiency of 10.206%, consistent with the simulation results, while maintaining a peak wavelength at 677.5 nm within a current range of 0-20 mA. Furthermore, the RCEPD exhibits a peak response wavelength at 678 nm, matching that of the RCLED. Utilizing RCLEPD as the sensor, photoplethysmography (PPG) signals are collected from the human wrist under different RCLED driving currents resulting in an average period of 977 ms which aligns with a human pulse frequency of 61 beats/min. With further processing techniques applied to PPG signals, RCLEPD is expected to be used as a sensor in wearable blood pressure and glucose monitoring devices.

Hyperspectral Imaging Aiding Artificial Intelligence: A Reliable Approach for Food Qualification and Safety

Hyperspectral imaging (HSI) is one of the non-destructive quality assessment methods providing both spatial and spectral information. HSI in food quality and safety can detect the presence of contaminants, adulterants, and quality attributes, such as moisture, ripeness, and microbial spoilage, in a non-destructive manner by analyzing spectral signatures of food components in a wide range of wavelengths with speed and accuracy. However, analyzing HSI data can be quite complicated and time consuming, in addition to needing some special expertise. Artificial intelligence (AI) has shown immense promise in HSI for the assessment of food quality because it is so powerful at coping with irrelevant information, extracting key features, and building calibration models. This review has shown various machine learning (ML) approaches applied to HSI for quality and safety control of foods. It covers the basic concepts of HSI, advanced preprocessing methods, and strategies for wavelength selection and machine learning methods. The application of HSI to AI increases the speed with which food safety and quality can be inspected. This happens through automation in contaminant detection, classification, and prediction of food quality attributes. So, it can enable decisions in real-time by reducing human error at food inspection. This paper outlines their benefits, challenges, and potential improvements while again assessing the validity and practical usability of HSI technologies in developing reliable calibration models for food quality and safety monitoring. The review concludes that HSI integrated with state-of-the-art AI techniques has good potential to significantly improve the assessment of food quality and safety, and that various ML algorithms have their strengths, and contexts in which they are best applied.

The Potential for Hyperspectral Imaging and Machine Learning to Classify Internal Quality Defects in Macadamia Nuts

Tree nuts are rich in nutrients, and global production and consumption have doubled during the last decade. However, nuts have a range of quality defects that must be detected and removed during post-harvest processing. Tree nuts can develop hidden internal discoloration, and current sorting methods are prone to subjectivity and human error. Therefore, non-destructive, real-time methods to evaluate internal nut quality are needed. This study explored the potential for VNIR (400–1000 nm) hyperspectral imaging to classify brown center disorder in macadamias. This study compared the accuracy of classifiers developed using images of kernels imaged in face-up and face-down orientations. Classification accuracy was excellent using face-up (>97.9%) and face-down (>94%) images using ensemble and linear discriminate models before and after wavelength selection. Combining images to form a pooled dataset also provided high accuracy (>90%) using artificial neural network and support vector machine models. Overall, HSI has great potential for commercial application in nut processing to detect internal brown centers using images of the outside kernel surface in the VNIR range. This technology will allow rapid and non-destructive evaluation of intact nut products that can then be marketed as a high-quality, defect-free product, compared with traditional methods that rely heavily on representative sub-sampling.

Temperature measurement method with line and continuum emissions in Ar-N2 DC arc

Abstract This study discusses a technique for accurate temperature measurement of thermal plasmas with a high-speed camera. Temperature of thermal plasmas is important parameter for plasma processes. High-speed cameras are useful to measure plasma temperatures in two dimensions. Measurements of plasma emission with a high-speed camera and band-pass filter provide only the spectral intensity of the entire transmitted wavelength range. Temperature measurements with high-speed cameras by Boltzmann plot method or Fowler-Milne method are less accurate. Theoretical consideration of the line and continuum emissions has improved the accuracy of plasma temperature measurement. The measurement target was a free burning arc in an argon and nitrogen atmosphere. Temperature errors were estimated based on the deviation from the true emission coefficient. The calculation and measurement errors were discussed as the factors of the deviation. Error estimation provides important insight into the selection of the measurement wavelength.

Grade Classification of Camellia Seed Oil Based on Hyperspectral Imaging Technology.

To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength. Combined with genetic algorithm (GA) and support vector machine algorithm (SVM), different grade classification models for camellia seed oil were developed using full wavelengths (GA-SVM), characteristic wavelengths (CARS-GA-SVM), and fusing spectral and image features (CARS-GLCM-GA-SVM). The results show that the CARS-GLCM-GA-SVM model, which combined spectral and image information, had the best classification effect, and the accuracy of the calibration set and prediction set of the CARS-GLCM-GA-SVM model were 98.30% and 96.61%, respectively. Compared with the CARS-GA-SVM model, the accuracy of the calibration set and prediction set were improved by 10.75% and 12.04%, respectively. Compared with the GA-SVM model, the accuracy of the calibration set and prediction set were improved by 18.28% and 18.15%, respectively. The research showed that hyperspectral imaging technology can rapidly classify camellia seed oil grades.

Design and Analysis of Ridge and Sub-Wavelength Grating Waveguide Based Micro Ring-Resonator Sensor

The subwavelength grating microring resonator based filter shows great promise in optical sensing and communication. However, it spectrum nature makes it unsuitable for tracking or extracting a single wavelength from a broadband light source. In this paper, we used side-mode suppression, based on an angular grating- subwavelength microring resonator which offer high flexibility in wavelength design within the silicon-on-insulator (SOI) waveguide. By periodically arranging silicon waveguide pieces in three different widths, we can set the effective index along the inner sidewall of subwavelength microring resonator. thereby can achieve the precise wavelength selection. We examine the effects of various key parameters on the center wavelength, side-mode suppressed ratio, and quality factor of this filter.The proposed structure's isalso analysed for coupling strength between the bus and subwavelength grating ring resonator. The wavelength selection, compact size, compatibility with other similar nature waveguide-based devices and design flexibility highlight its significant potential in compact optical sensing and optical communication

Smart mathematically filtered UV spectroscopic methods for quality assurance of rosuvastatin and valsartan from formulation

Abstract Valsartan and rosuvastatin together in a binary form have been utilized to reduce hypertension and hyperlipidemia to control cardiovascular complications. This study depicts the simple three mathematically manipulated UV spectroscopic techniques for the estimation of rosuvastatin and valsartan in the formulation. The first method is simple UV absorption at 310 nm by RST and the first derivatization method for VTN. Determining the magnitude difference of a ratio spectrum at two identified wavelengths is the second approach, and determination of the magnitude of the first derivatives of the ratio spectra of RST and VTN constitute the third technique. The selection of wavelengths, divisor concentrations, and peak amplitudes were optimized and validated. The straight line was constructed in the range of 1–30 and 2–25 µg/ml for RST and VST by the normal and first derivatization method. By using the magnitude difference and magnitude of first derivative ratio spectra approaches, the concentrations of 1–12 and 2–25 µg/ml for RST and VTN, respectively, displayed a straight line. The limit of quantification was less than 1 µg/ml for RST and less than 2 µg/ml for VTN. It was eventually found that the accuracy, expressed as a percentage recovery, ranged between 98.94 and 99.55% for RST and 100.36 and 101.08% for VTN. The % RSD did not exceed 1.82 and 1.91 for RST and VTN, respectively. The three techniques were used to accurately measure RST and VTN in their binary formulations and physically mixed solutions, and the results were statistically compared to the previously published HPLC technique. The outstanding recovery achieved by using the authentic standard addition approach validated the methods’ supplemental accurateness. The Analytical Greenness and Red Green Blue procedures verified the eco-friendliness of the suggested UV spectroscopic approaches, which were also found to be superior to the documented HPLC methods.

Rapid on-site multi-indicator detection of Polygonatum cyrtonema Hua by handheld near-infrared spectroscopy with wavelength selection

The rapid, accurate, and on-site quantitative evaluation of the 3 components (total polysaccharide, total flavonoid, and total saponin) of Polygonatum cyrtonema Hua (PCH) is of great significance to ensure the value of its medicine and food, for this purpose, a handheld near-infrared (NIR) spectrometer was used. The raw NIR spectra were preprocessed by seven methods and their combinations, then three kinds of wavelength selection algorithms, the competitive adaptive reweighted sampling (CARS), random frog (RF) sampling, and Monte Carlo uninformative variable elimination (MCUVE) were applied, and the characteristic wavelengths for total polysaccharide, total saponin, and total flavonoid in PCH were obtained, respectively. Subsequently, the partial least squares (PLS) regression models were developed. The results showed that the combination of first derivative and standard normal variate was the optimal pretreatment method, and CARS had the highest performance of the wavelength selection for total polysaccharide and total flavonoid, while RF was the highest for total saponin. With these selected wavelengths, 3 PLS prediction models were built, and their performances were validated with unknown samples, the results showed that the determination coefficient of the prediction set (R2P) of total polysaccharide, total saponin, and total flavonoid were 0.980, 0.948, and 0.942, respectively, and the corresponding root mean square error of prediction set (RMSEP) were 0.162, 0.142, and 0.052. Furthermore, the residual prediction deviations (RPDs) of the three components were all larger than 3, demonstrating the prediction performance of the models was high. Therefore, the three active functional components in PCH could be rapidly determined by a handheld NIR spectrometer, which would be beneficial for ensuring the quality of PCH.

A CARS-SPA-GA Feature Wavelength Selection Method Based on Hyperspectral Imaging with Potato Leaf Disease Classification.

Early blight and ladybug beetle infestation are important factors threatening potato yields. The current research on disease classification using the spectral differences between the healthy and disease-stressed leaves of plants has achieved good progress in a variety of crops, but less research has been conducted on early blight in potato. This paper proposes a CARS-SPA-GA feature selection method. First, the raw spectral data of potato leaves in the visible/near-infrared light region were preprocessed. Then, the feature wavelengths were selected via competitive adaptive reweighted sampling (CARS) and the successive projection algorithm (SPA), respectively. Then, the two sets of wavelengths were reorganized and duplicates were removed, and secondary feature selection was conducted with genetic algorithm (GA). Finally, the feature wavelengths were fed into different classifiers and the parameters were optimized using a real-coded genetic algorithm (RCGA). The experimental results show that the feature wavelengths selected by the CARS-SPA-GA method accounted only for 9% of the full band, and the classification accuracy of the RCGA-optimized support vector machine (SVM) classification model reached 98.366%. These results show that it is feasible to classify early blight and ladybug beetle infestation in potato using visible/near-infrared spectral data, and the CARS-SPA-GA method can substantially improve the accuracy and detection efficiency of potato pest and disease classification.

Quantitative Prediction of Acid Value of Camellia Seed Oil Based on Hyperspectral Imaging Technology Fusing Spectral and Image Features.

Acid value (AV) serves as an important indicator to assess the quality of oil, which can be used to judge the deterioration of edible oil. In order to realize the quantitative prediction of the AV of camellia seed oil, which was made from camellia oleifolia, hyperspectral data of 168 camellia seed oil samples were collected using a hyperspectral imaging system, which were related to their AV content measured via classical chemical titration. On the basis of hyperspectral full wavelengths, characteristic wavelengths, and fusing spectral and image features, the quantitative prediction AV models for camellia seed oil were established. The results demonstrating the 2Der-SPA-GLCM-PLSR model fusing spectral and image features stood out as the optimal choices for the AV prediction of camellia seed oil, with the correlation coefficient of calibration set (Rc2) and the correlation coefficient of prediction set (Rp2) at 0.9698 and 0.9581, respectively. Compared with those of 2Der-SPA-PLSR, the Rc2 and Rp2 were improved by 2.11% and 2.57%, respectively. Compared with those of 2Der-PLSR, the Rc2 and Rp2 were improved by 5.02% and 5.31%, respectively. Compared with the model based on original spectrum, the Rc2 and Rp2 were improved by 32.63% and 40.11%, respectively. After spectral preprocessing, characteristic wavelength selection, and fusing spectral and image features, the correlation coefficient of the optimal AV prediction model was continuously improved, while the root mean square error was continuously decreased. The research demonstrated that hyperspectral imaging technology could precisely and quantitatively predict the AV of camellia seed oil and also provide a new environmental method for detecting the AV of other edible oils, which is conducive to sustainable development.

Quantitative Analysis of High-Price Rice Adulteration Based on Near-Infrared Spectroscopy Combined with Chemometrics.

Near-infrared spectroscopy (NIRS) holds significant promise in detecting food adulteration due to its non-destructive, simple, and user-friendly properties. This study employed NIRS in conjunction with chemometrics to estimate the content of low-price rice flours (Nanjing, Songjing, Jiangxi silk, Yunhui) blended with high-price rice (Wuchang and Thai fragrant). Partial least squares regression (PLSR), support vector regression (SVR), and back-propagation neural network (BPNN) models were deployed to analyze the spectral data of adulterated samples and assess the degree of contamination. Various preprocessing techniques, parameter optimization strategies, and wavelength selection methods were employed to enhance model accuracy. With correlation coefficients exceeding 87%, the BPNN models exhibited high accuracy in estimating adulteration levels in high-price rice. The SPXY-SG-BPNN, SPXY-MMN-BPNN, KS-SNV-BPNN, and SPXY-SG-BPNN models showcased exceptional performance in discerning mixed Wuchang japonica, Thai fragrant indica, and Thai fragrant Yunhui rice. As shown above, NIRS demonstrated its potential as a rapid, non-destructive method for detecting low-price rice in premium rice blends. Future studies should be performed to concentrate on enhancing the models' versatility and practical applicability.

Predicting water content in engine oil using SPA-ISSA-BP methods  

Contamination of engine oil with water can accelerate oil oxidation and decomposition, resulting in oil degradation. Therefore, the detection and prediction of water content in engine oil is crucial to ensure the long-term safe operation of engines. In this study, near-infrared spectroscopy was used to analyse engine oils with varying water contents. Spectral data were obtained, and various methods, including Savitzky-Golay (SG) smoothing, orthogonal signal correction (OSC), and successive projection algorithm (SPA) were employed to compare the performance differences of principal component regression models. An Improved Sparrow Searches Algorithm (ISSA) was then applied to optimise the Back-Propagation Neural Network (BPNN) for predicting water content in SAE 10W-30 engine oil. The results showed that the performance of the principal component regression model constructed from spectral data after SG smoothing, OSC, and the SPA feature wavelength selection had improved. The BPNN optimised by the improved Sparrow Search Algorithm accelerated the convergence speed of the model and effectively improved the prediction accuracy of the BPNN. The obtained coefficient of determination (R2) was 0.99103, the root mean square error (RMSE) was 2. 2136 10-4, and the mean absolute error (MAE) was 1. 8889×10-4. These results provide an effective method for detecting and predicting the water content in engine oil.

Near-infrared spectroscopy combined with support vector machine for the identification of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) adulteration using wavelength selection algorithms

The frequent occurrence of adulterating Tartary buckwheat powder with crop flours in the market necessitates an urgent need for a simple analysis method to ensure the quality of Tartary buckwheat. This study employed near-infrared spectroscopy (NIRS) for the collection of spectral data from Tartary buckwheat samples adulterated with whole wheat, oat, soybean, barley, and sorghum flours. The competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were deployed to identify informative wavelengths. By integrating support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA), we constructed qualitative models to discern Tartary buckwheat adulteration. The PLS-DA model exhibited prediction accuracies between 89.78 % and 94.22 %, while the mean-centering (MC)-PLS-DA model showcased impressive predictive accuracy of 93.33 %. Notably, the feature-based Autoscales-CARS-CV-SVM model achieved more excellent identification accuracy. These findings exhibit the excellent potential of chemometrics as a powerful tool for detecting food product adulteration.

Non-destructive detection of male and female information in ducklings based on near-infrared spectral wavelength selection and deep learning

The technology for sex identification in ducklings can contribute to increased revenue and cost savings in modern duck farming. However, traditional manual identification techniques require high skill levels and involve significant labor intensity. In this study, a non-destructive, user-friendly, and efficient duckling sex identification technique was proposed using near-infrared spectroscopy and deep learning algorithms. Spectral data from 600 groups of newly hatched ducklings were collected. These data were divided into training, testing, and validation sets in a ratio of 7:2:1. The raw spectral data was preprocessed using the Savitzky-Golay convolution derivative method, which was employed for subsequent spectral feature wavelength extraction and modeling. The characteristic wavelengths were extracted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination combined with SPA (UVE-SPA). Conventional machine learning methods − support vector machine (SVM) and different deep learning models, including multilayer perceptron (MLP), mobile network version 2 (MobileNetV2), and residual neural network (ResNet), were studied and compared. The experiment showed that deep learning algorithms outperform traditional spectral analysis models in terms of classification performance. Furthermore, conducting feature wavelength extraction before constructing the classification model could reduce the model’s testing time and even improve its classification performance. Finally, the four models with better classification performance were validated using a validation set, and the combination of MobileNetV2 model UVE-SPA was selected as the optimized model for ducklings’ gender determination, with a classification accuracy of 98.3 % and an average validation time of 1.1 ms. In summary, the detection model established using near-infrared spectroscopy and MobileNetV2 can achieve non-destructive identification of the gender of ducklings. The findings can provide a preliminary research foundation and technical support for the subsequent design of related online intelligent detection systems.

Investigating the Influence of Wavelength-Specific Textured Backgrounds in Background Oriented Schlieren (BOS) Imaging

This study investigates the influence of wavelength-specific textured backgrounds on the effectiveness of Background-Oriented Schlieren (BOS) imaging, focusing on wavelengths from 400 nm to 670 nm at intervals of 30 nm intervals and multiple captured recordings for each background wavelength interval. By analyzing the signal-to-noise ratio (SNR) computationally, and the image gradient magnitude, we aimed to determine the optimal wavelengths for capturing turbulence and determine the effectiveness of colored backgrounds in natural external environments for schlieren. The SNR, calculating the ratio of mean signal intensity to noise standard deviation, revealed the highest value at 550 nm (SNR = 22.8), indicating maximized clarity. Similarly, image gradient magnitude, computed using the Sobel operator to assess spatial intensity changes, peaked at 550 nm (G=52.3), confirming effective turbulence visualization. Our findings align with the Bayer color filter trend, suggesting that the green spectrum is particularly advantageous for BOS imaging. Deviations at 490 nm and 580 nm, characterized by lower SNR and gradient magnitude, could be attributed to atmospheric scattering, refractive index overlap, or slight digital video capture differences., highlighting environmental factors that can influence imaging performance and value variation. These insights emphasize the importance of wavelength selection and background design in real-world BOS applications, suggesting that while 550 nm provides optimal results, further refinement may enhance the effectiveness of other wavelengths.

Towards a Spectral Library of Medicinal and Aromatic Plant species (MAPs): Plant Discrimination and Wavelength Selection

The recognition and identification of Medicinal and Aromatic Plants species (MAPs) is a challenge for researchers and professionals in the field. To address this issue, this study aimed to examine the potential of using hyperspectral remote sensing, chemometrics and Machine Learning in discriminating between MAP species. Specifically, we tested the applicability of UV-Near-infrared Spectroscopy (UV-NIR), PCA and Partial Least-Squares Discriminant Analysis (PLS-DA) as tools for discrimination. The spectral signature data were obtained by UV-near infrared spectroscopy, covering a range of 325–1075 nm. Principal component analysis (PCA) was applied to reduce data dimensionality in order to minimize errors in PLS-DA model. This model was used to build a discriminant model based on the wavelengths obtained from PCA. The results show that it is effective to combine UV-NIR spectral features and PLS-DA to establish a discriminant model for accurately classifying the MAP species. The key wavelengths most involved in the discrimination were found in the NIR region and especially in ranges 516–548 and 628–1070 nm.

Optical resonant cavities carving pathways in tunable wavelength sensitive visible-NIR organic photodetectors

Enhancing the photodetection capabilities of organic photodetectors (OPDs) is crucial for advancing applications in medical monitoring, optical communications, image sensing, and robotics, where a strong, focused peak response at a specific designed wavelength is essential for improving sensitivity, wavelength selectivity, and resolution in imaging systems. By controlled integration of ZnO layers within PBDBT:BTP-4F-based OPDs to form a Fabry-Pérot optical cavity, we developed a cost-effective approach to fabricating highly sensitive OPDs by utilizing PBDBT:BTP-4F organic bulk heterojunctions, and extended its detection wavelengths into the near-infrared (NIR) range. Our design integrates a single silver (Ag) layer that significantly enhances peak detection at a wavelength of 830 ​nm, resulting in a remarkably narrow full-width at half maximum (FWHM) wavelength of 30 ​nm and yielding a photoresponse ten times greater than that of non-resonant devices. Furthermore, by varying the thickness of the ZnO layer from 77 ​nm to 620 ​nm, we achieve high spectral tunability, allowing fine adjustments of the resonant peak across a spectrum ranging from ultraviolet (UV) and visible to NIR wavelengths. This sensitive photodetector is also well-suited for applications in photoplethysmography (PPG), effectively detecting pulse signals in the NIR spectrum which has significant potential in medical diagnostics. This work advances the integration of cost-effective, wavelength-selective spectroscopic visible-NIR OPDs, paving the way for the next generation of sensitive photodetectors.