Wavelength Selection Research Articles

Identification of chrysanthemum variety via hyperspectral imaging and wavelength selection based on multitask particle swarm optimization

Chrysanthemum, a widely favored flower tea, contains numerous phytochemicals for health benefits. Due to the different geographical origins and processing technics, its variety has a direct influence on the phytochemical content and pharmacological effect. Accordingly, an accurate identification for chrysanthemum varieties is significant for quality detection and market supervision. In this study, the hyperspectral imaging (HSI) combined with chemometrics methods was exploited to identify the chrysanthemum varieties. First, to alleviate the problem of easily trapping into local optimum in traditional spectral variable selection methods, the multi-tasking particle swarm optimization (MTPSO) was developed to select the key wavelengths by dividing hundreds of variables into low-dimensional subtasks. Second, to enrich the feature information, the spatial texture and color features contained in hyperspectral images were extracted and applied to chrysanthemum identification for the first time. Finally, an ensemble learning model, extreme gradient boosting (XGBoost), was constructed to conduct the chrysanthemum variety classification due to its strong generalization ability. Experimental results showed that the proposed MTPSO achieved the identification accuracy of 96.89%, and increased by 1.11–5.91% than classical spectral feature selection methods. Furthermore, after the involvement of spatial image information, the classification accuracy using spatial–spectral features was improved further, and reached 98.39%. Overall, this study highlights that the feature fusion of key wavelengths and spatial information is more effective for chrysanthemum variety identification, and can also provide technical reference for other HSI-related applications.

Highly flexible and temperature-tolerant phase change devices for dual-band camouflage

Here, we have developed a more temperature-tolerant emitter with a gradient emittance, which can enable adaptation to changing environmental conditions. Such a thermal emitter is mainly constructed by multilayered films composed of nitrogen (N)-doped Ge2Sb2Te5 (N-GST) and an underlying metal film. The proposed device not only possesses special wavelength selectivity in the middle infrared range but can also dynamically adjust average emissivity (from 0.13 to 0.83) through the degree of crystallization. Besides, N doping can elevate the phase transition temperature of GST and enhance its thermal resistance, which renders it particularly well-suited for applications in higher temperature environments than pure GST. This emitter also shows strong adhesion capability and high flexibility against bending, enabling more practical and widespread situations. By using a multi-layer structure, we combined the more temperature-tolerant and dynamically modulating N-GST emitter with an optical thin film, successfully achieving dual camouflage for both infrared and visible light. The element doping technology and multi-layer stacking approach presented in this research will provide valuable insight for the development of dynamic emissive materials in multi-spectral camouflage scenarios.

Comparison of qualitative and quantitative performance of two portable near-infrared spectrometers for intact Rehmanniae Radix and calibration transfer

As a Chinese herbal medicine with high medical value, Rehmanniae Radix (RR) has a wide variety of geographical origins leading to distinctly diverse quality, and moisture content during storage affects the critical active ingredient content. In this work, two portable near-infrared (NIR) spectrometers, DLP NIRscan Nano (DLP) and VIAVI MicroNIR 1700 (M1700) were used for in-situ spectral acquisition from intact RR. The partial least squares-discriminant analysis (PLS-DA) was employed for three geographical sources (Shandong, Shanxi, Henan). After multiple pre-processing and wavelength selection, the M1700-based PLS-DA model, accuracy (Acc), sensitivity (Sen), and specificity (Spe), achieved 100 % accuracy. In contrast, model predictions using the low-cost instrument (DLP) was not satisfactory. The partial least squares regression (PLSR) was applied to predict the moisture content of RR. The best correlation coefficients in the prediction set (R2p), Root Mean Square Error of Prediction (RMSEP), and residual prediction deviation (RPD) values (0.98, 0.98, 4.98) were obtained by DLP and were also employed for comparison with the M1700 model (0.99, 0.70, 7.00). Therefore, to further improve the model prediction effect of the low-cost DLP, we employed the improved principal component analysis (IPCA), direct standardization (DS), and piecewise direct standardization (PDS) calibration transfer techniques. Unequivocally, IPCA optimized the origin identification model (Acc, Sen, and Spe were all 1.00), the moisture content prediction model RPD was increased by 23 %, and its RMSEP was reduced by 18 %, leading to the improvement in prediction accuracy of the DLP model. This study provides a portable and low-cost detection method for the in-situ evaluation of the quality of RR quality and a feasible solution for the NIR techniques to be used in the rapid and accurate in-situ analysis of RR.

Optoelectronic properties of electron-acceptor molecules adsorbed on graphene/silicon carbide interfaces

Silicon carbide has emerged as an optimal semiconducting support for graphene growth. In previous studies, the formation of an interfacial graphene-like buffer layer covalently bonded to silicon carbide has been observed, revealing electronic properties distinct from ideal graphene. Despite extensive experimental efforts dedicated to this interface, theoretical investigations have been confined to its ground state. Here, we use many-body perturbation theory to study the electronic and optical characteristics of this interface and demonstrate its potential for optoelectronics. By adsorbing graphene, we show that the quasiparticle band structure exhibits a reduced bandgap, associated with an optical onset in the visible energy window. Furthermore, we reveal that the absorption of two prototypical electron-accepting molecules on this substrate results in a significant renormalization of the adsorbate gap, giving rise to distinct low-lying optically excited states in the near-infrared region. These states are well-separated from the substrate’s absorption bands, ensuring wavelength selectivity for molecular optoelectronic applications.

Near-infrared spectroscopy identification method of cashmere and wool fibers based on an optimized wavelength selection algorithm

Cashmere and wool fibers have similar chemical compositions, making them difficult to distinguish based on their absorption peaks and band positions in near-infrared spectroscopy. Existing studies commonly use wavelength selection or feature extraction algorithms to obtain significant spectral features, but traditional algorithms often overlook the correlations between wavelengths, resulting in weak adaptability and local optimum issues. To address this problem, this paper proposes a recognition algorithm based on optimal wavelength selection, which can remove redundant information and make the model effective in capturing patterns and key features of the data. The wavelengths are rearranged by computing the information gain ratio for each wavelength. Then, the sorted wavelengths are grouped based on equal density, which ensures that all wavelengths within each group have equal information and avoids over-focusing on individual groups. Meanwhile, the group genetic algorithm is used to find the wavelengths with highly informative and search optimal grouped combinations, in order to explore the entire spectrum wavelength. Finally, combined with a partial least squares discriminant analysis(PLS-DA) model, the recognition accuracy reached 97.3 %. The results indicate that, compared to traditional methods such as CARS, SPA, and GA, our method effectively reduces redundant information, selects fewer but more informative wavelengths, and improves classification accuracy and model adaptability.

Nature of instability in flow-driven porous anodic oxide.

Self-organized porous anodic oxide films are formed by the electrochemical oxidation of reactive metal aluminum in acidic solutions in which the oxide is soluble. Recently, viscous flow models have shown using linear stability analysis that the instability results from a trade-off between the destabilizing effect of viscous flow of oxide and the stabilizing effect of oxide formation, which provides the wavelength selection mechanism for pattern formation. Anion adsorption on surface growth sites causes nonuniform compressive stress at the oxide-solution interface, which drives the flow. This anodic instability is analogous to the classical Marangoni instability induced by surface tension gradients. In this work, nature of the instability beyond the stability threshold is determined using a weakly nonlinear analysis. For the growth of well-developed pores beyond the threshold, a subcritical nature of the instability is essential. However, our weakly nonlinear analysis shows that the solutions emerging from neutral stability are supercritical in nature at all wavenumbers for the practical range of anodizing control parameters investigated. We also determine the region where the model is Hadamard stable, a necessary condition for well-posedness.

Quality assessment of Gastrodia elata Blume (Tianma) based on Vis-NIR spectroscopy: Discrimination of harvest times and prediction of quality indicator contents

Gastrodia elata Blume (Tianma) is widely cultivated and consumed as a food and herbal material in China. With its increasing use as a functional food, Tianma is gaining popularity in the market. However, during the cultivation process, the harvest time affects its maturity and storage time, while the total content of gastrodin (GAS) and p-hydroxybenzyl alcohol (HA) affects its nutritional value, making it difficult to control the quality of Tianma. In this study, a combination of visible-near-infrared (Vis-NIR) spectroscopy and chemometrics was used to identify the harvest time and determine the quality indicator content. A Vis-NIRRamanNet modeling method based on the architecture of RamanNet was proposed and compared with harvest time discrimination models constructed using partial least squares discriminant analysis (PLS-DA), k-nearest neighbor (KNN), support vector machine (SVM), and one-dimensional convolutional neural network (1D-CNN). The results demonstrated that Vis-NIRRamanNet exhibited superior performance, achieving accuracy rates of 0.995 and 0.973 on the training and testing sets, respectively. Subsequently, Vis-NIR spectroscopy combined with support vector regression (SVR) was employed for quantitative analysis of quality indicator content. Wavelength selection methods such as uninformative variable elimination (UVE), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS) were utilized to optimize the model. The results indicated that the CARS-SVR model provided the best prediction of quality indicator content, with root mean square error of prediction (RMSEP) and coefficient of determination (Rp2) values of 0.257 and 0.901, respectively. Therefore, the combination of Vis-NIR spectroscopy and chemometrics shows significant potential in assisting the selection of high-quality Tianma.

Quality by Design Approach for the Stability Indicating Method Development and Validation of Selpercatinib Drug Formulation by using RP-HPLC

In method development quality design approach is useful for method goal identification evaluation, method selection and risk assessment. Selpercatinib is a drug used in the treatment of certain types of cancer. The method development includes selecting appropriate chromatographic conditions such as the mobile phase composition, column, and selection wave length. The aim is to achieve optimum separation and quantification of selpercatinib in the dosage form. Optimizing chromatographic conditions by using design expert software. The mobile phase methanol and 0.1% Acetic acid are used. The mobile phase range is 80:20 v/v. The range of this mobile phase is 80:20 .0.7 mL/min found flow rate. The retention time of selpercatinib is 3.20 minutes. The assay was found to be 99.01% for selpercatinib. Optimization was performed. Factorial design performed, i.e., flow rate and percentages of methanol. Analysis of variance confirmed that method parameters. UV detection at 220 nm. The International Council of Harmonization (ICH) guidelines ensure the accurate, precise and reliable results to validate the parameters is an acceptable range. Stress studies reveal that drugs were degraded acidic conditions, alkaline conditions, neutral conditions, oxidative conditions, and photolytic conditions. Hence the proposed method was stability, indicating using quality by design (QbD) approach all the method parameters were better understood. This systematic approach ensures a thorough understanding of the assay, facilitates process optimization, and enables effective control strategies to reduce variability and enhance product quality.

Analytical Method Development by Using UV-Spectrophotometer for Estimation of Valsartan in Bulk

In order to accurately determine valsartan (API), this work aims to build a straightforward and exact UV spectroscopic approach. To improve sensitivity and accuracy, the procedure entails methodically optimizing experimental parameters, including solvent choice and wavelength selection. The produced stock solutions were tested for absorbance at 249 nm using a UV spectrophotometer. With %RSD values below 2% for both intra- and inter-day, the devised approach was determined to be quite accurate. At each additional concentration, the approach was shown to be accurate, yielding good drug recoveries ranging from 98 to 101%. The process was validated for linearity, specificity, accuracy, precision and limit of detection (LoD) and limit of quantification (LoQ) as per International Council of Harmonization (ICH) criteria. Its dependability for quantitative analysis of valsartan in pharmaceutical formulations was demonstrated. This UV spectroscopic approach offers a cost-effective and efficient alternative for routine quality control in pharmaceutical industries.

Thermodynamic theorical and parametric analysis of a photovoltaic-photo/thermochemistry synergistic conversion system with full spectrum cascading utilization

Solar spectral splitting and cascade utilization is an efficient way to improve solar energy conversion efficiency. This study analyzes the photovoltaic-photo/thermochemistry synergistic conversion system from a thermodynamic perspective. The photo/thermochemistry conversion combined with photochemistry and thermochemistry can effectively utilize the residual spectrum that photovoltaic cannot efficiently use. Through the established thermodynamic model, the thermodynamic limitation of the photovoltaic-photo/thermochemistry synergistic conversion system is calculated. The analysis examines the effects of parameters such as cutoff wavelengths, concentration ratios, and internal irreversibility on system performance, including photovoltaic output voltage, available energy per absorbed photon of photocatalysis, conversion temperature, and selective absorption cutoff wavelength. The results indicate a high thermodynamic limitation of 47.1% at one sun and 68.56% at full concentrations. Energy and exergy flow analysis identifies the proportion of system energy and exergy loss. Compared with other spectral splitting systems, the proposed system demonstrates a significant advantage in solar cascading utilization. This study provides a thermodynamic foundation for the development and design of photovoltaic-photo/thermochemistry synergistic conversion systems.

Rapid and nondestructive watermelon (Citrullus lanatus) seed viability detection based on visible near-infrared hyperspectral imaging technology and machine learning algorithms.

The improper storage of seeds can potentially compromise agricultural productivity, leading to reduced crop yields. Therefore, assessing seed viability before sowing is of paramount importance. Although numerous techniques exist for evaluating seed conditions, this research leveraged hyperspectral imaging (HSI) technology as an innovative, rapid, clean, and precise nondestructive testing method. The study aimed to determine the most effective classification model for watermelon seeds. Initially, purchased watermelon seeds were segregated into two groups: One underwent sterilization in a dehydrator machine at 40°C for 36h, whereas the other batch was stored under favorable conditions. Watermelon seeds' spectral images were captured using an HSI with a charge-coupled device camera ranging from 400 to 1000nm, and the segmented regions of all samples were measured. Preprocessing techniques and wavelength selection methods were applied to manage spectral data workload, followed by the implementation of a support vector machine (SVM) model. The initial hybrid-SVM model achieved a predictive accuracy rate of 100%, with a test set accuracy of 92.33%. Subsequently, an artificial bee colony (ABC) optimization was introduced to enhance model precision. The results indicated that, with kernel parameters (c, g) set at 13.17 and 0.01, respectively, and a runtime of 4.19328s, the training and evaluation of the dataset achieved an accuracy rate of 100%. Hence, it was practical to utilize HSI technology combined with the PCA-ABC-SVM model to detect different watermelon seeds. As a result, these findings introduce a novel technique for accurately forecasting seed viability, intended for use in agricultural industrial multispectral imaging. PRACTICAL APPLICATION: The traditional methods for determining the condition of seeds primarily emphasize aesthetics, rely on subjective assessment, are time-consuming, and require a lot of labor. On the other hand, HSI technology as green technology was employed to alleviate the aforementioned problems. This work significantly contributes to the field of industrial multispectral imaging by enhancing the capacity to discern various types of seeds and agricultural crop products.

BiPLS-RF: A hybrid wavelength selection strategy for laser induced fluorescence spectroscopy of power transformer oil

In this paper, a method for indirect diagnosis of transformer faults based on the fluorescence spectrum and characteristic wavelength screening of transformer oil has been proposed. Specifically, a hybrid strategy (BiPLS-RF) for establishing the fluorescence spectrum feature screening of transformer oil using backward interval partial least squares (BiPLS) and random forest (RF) has been proposed. Aiming at the problem of transformer fault diagnosis, the laser induced fluorescence (LIF) spectroscopy of transformer oil in different states was first collected, and it is found that the fluorescence spectrum intensity of normal transformer oil was stronger than that of faulty transformer oil. Then the characteristic bands of the original fluorescence spectra were screened by BiPLS. It is found that when the original fluorescence spectra were divided into 15 sub-intervals, the minimum root mean squares error of cross-validation can be obtained by selecting 3 sub-intervals (including 411 wavelengths). On this basis, RF was employed to further screen the characteristic wavelengths and realized the identification of the fluorescence spectrum. It is found that in the RF model composed of 54 trees, the selected 196 characteristic wavelengths of the fluorescence spectrum can minimize the analysis error (0.56%). In addition, the selected characteristic wavelength information was fed into other common classifiers to construct a fluorescence spectrum identification model, which further proved the effectiveness of BiPLS-RF for wavelength selection for LIF spectroscopy of power transformer oil. The results show that it is feasible to use BiPLS-RF to screen the characteristic wavelength of LIF spectroscopy and apply it to transformer fault diagnosis, which provides a new solution for transformer fault diagnosis.

Short-wavelength infrared light sensing using an electrostatic MEMS resonator integrated with a plasmonic optical absorber

Near-infrared light detectors are employed in various fields such as food inspection for spectroscopic measurements. Despite the fact that photodiodes made of compound semiconductors are used in conjunction with optical filters or monochromators for highly sensitive and wavelength-selective detection of near-infrared light, improvements in device size and cost are necessary for widespread application. Therefore, we propose the use of silicon microelectromechanical systems (MEMS) sensors. This study proposes a single-crystalline Si electrostatic MEMS resonator sensor that is integrated with a gold nanostructure near-infrared light absorber. The device utilizes the resonance frequency shift that occurs due to the thermal stress caused by the heat generated by the near-infrared light on the resonator. A gold nanostructure absorber is mounted on the resonator to enhance photothermal conversion efficiency, enabling the detection of near-infrared light with wavelength selectivity. We fabricated an Au nanostructure-mounted electrostatic transducer and evaluated its performance by measuring its resonance under near-infrared light irradiation. In a light intensity detection experiment, it was found that the relative resonant frequency shift increased linearly with the input in the range below 100 μW, which is consistent with the trend against the input heat. The minimum detection resolution of the light intensity was approximately 5 nW, and the minimum detectable intensity was 3.8 nW. The linear relationship between the optical absorption coefficient and the relative resonance frequency shift was clarified. An array of the devices with different absorption wavelength bands can be used to realize an ultra-compact spectroscopic analysis device without the need for traditional optical filters or monochromators.

Feasibility of a localized mode analysis method in an SOI platform based on carrier grating

In order to measure the intensity of modes that are transmitted inside the devices on the silicon-on-insulator (SOI) platform, researchers usually use pre-processed couplers to make the optical modes diffract out of the chip. However, the output couplers have an influence (e.g., attenuation and wavelength selectivity) on the modes of concern. Besides, as the quantity and variety of devices integrated into the SOI platform continue to escalate, the traditional method also shows limits on detecting devices far from the chip edge. So, is it feasible to directly and locally measure one specific mode’s intensity on some waveguide-based devices like the directional coupler, polarization beam splitter, and so on. Interference of two coherent pump beams has the capability to induce a periodic carrier distribution in the material, thus modulating the refractive index, effectively creating a temporary and erasable diffraction grating. In this study, an off-chip, non-destructive, and localized detection method based on carrier grating is proposed. A theoretical model is developed to calculate carrier dynamics under various pump configurations. Leveraging the finite-difference time-domain (FDTD) method and accounting for free carrier index (FCI) and free carrier absorption (FCA) effects, analysis of the quantitative impact of pump intensity and radius on the diffraction efficiency of the carrier grating in the silicon-on-insulator (SOI) platform and its far-field divergence characteristics is provided. Ultimately, this research contributes to a discussion on several commonly used application scenarios and the feasibility of experimental approaches. A spatial resolution of less than 10 µm and a diffraction efficiency of −15dB while simultaneously maintaining a far-field divergence of 7.8° for the SOI platform are proposed at the end of this article.

Rapid Measurement of Antioxidant Properties of Dendrobium officinale Using Near-Infrared Spectroscopy and Chemometrics.

Dendrobium officinale (D. officinale), often used as a dual-use plant with herbal medicine and food applications, has attracted considerable attention for health-benefiting components and wide economic value. The antioxidant ability of D. officinale is of great significance to ensure its health care value and safeguard consumers' interests. However, the common analytical methods for evaluating the antioxidant ability of D. officinale are time-consuming, laborious, and costly. In this study, near-infrared (NIR) spectroscopy and chemometrics were employed to establish a rapid and accurate method for the determination of 2,2'-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity, and ferric reducing antioxidant power (FRAP) in D. officinale. The quantitative models were developed based on the partial least squares (PLS) algorithm. Two wavelength selection methods, namely the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) method, were used for model optimization. The CARS-PLS models exhibited superior predictive performance compared to other PLS models. The root mean square errors of cross-validation (RMSECVs) for ABTS, FRAP, and DPPH were 0.44%, 2.64 μmol/L, and 2.06%, respectively. The results demonstrated the potential application of NIR spectroscopy combined with the CARS-PLS model for the rapid prediction of antioxidant activity in D. officinale. This method can serve as an alternative to conventional analytical methods for efficiently quantifying the antioxidant properties in D. officinale.

Enhanced prediction of cement raw meal oxides by near-infrared spectroscopy using machine learning combined with chemometric techniques.

The component analysis of raw meal is critical to the quality of cement. In recent years, near-infrared (NIR) has been emerged as an innovative and efficient analytical method to determine the oxide content of cement raw meal. This study aims to utilize NIR spectroscopy combined with machine learning and chemometrics to improve the prediction of oxide content in cement raw meal. The Savitzky-Golay convolution smoothing method is applied to eliminate noise interference for the analysis of calcium carbonate ( ), silicon dioxide ( ), aluminum oxide ( ), and ferric oxide ( ) in cement raw materials. Different wavelength selection techniques are used to perform a comprehensive analysis of the model, comparing the performance of several wavelength selection techniques. The back-propagation neural network regression model based on particle swarm optimization algorithm was also applied to optimize the extracted and screened feature wavelengths, and the model prediction performance was checked and evaluated using and RMSE. In conclusion, the results indicate that NIR spectroscopy in combination with ML and chemometrics has great potential to effectively improve the prediction performance of oxide content in raw materials and highlight the importance of modeling and wavelength selection techniques. By enabling more accurate and efficient determination of oxide content in raw materials, NIR spectroscopy coupled with meta-modeling has the potential to revolutionize quality assurance practices in cement manufacturing.

Hybrid wavelength selection strategy combined with ATR-FTIR spectroscopy for preliminary exploration of vintage labeling traceability of sauce-flavor baijiu

The allure of substantial profits has perpetuated the illicit trade of counterfeit vintage labels for baijiu. While various approaches have been employed to intelligently ascertain the vintage of baijiu, many of them are both cost-intensive and time-consuming. This work pioneered the use of Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric analysis, offering a non-destructive and economically viable method for discriminating sauce-flavor baijiu across different aging periods (1-, 2-, and 3-year). In this research, principal component analysis (PCA) was first conducted to explore clustering trends among distinct vintage groups. Subsequently, the effect of spectral pre-processing on modeling performance was explored. For wavelength selection, four wavelength selection methods (ReliefF, random forest variable importance (RFVI), variable importance in projection (VIP), and Venn) were first used to identify the subset of candidate features that potentially best mapped the vintage labels. Immediately following this, to explore the possibility of further improving the identification capabilities of the model as well as to reduce the redundant data that may still be present, sequential backward selection (SBS) was utilized for secondary feature reduction within the subset of candidates. The amalgamation of these two techniques is termed a “hybrid wavelength selection strategy.” Additionally, the dimensionality reduction effects of PCA and kernel principal component analysis (KPCA) were compared to demonstrate the robustness of the proposed method. Finally, classification models such as partial least squares discriminant analysis (PLS-DA), random forest (RF), and grasshopper optimization algorithm-based support vector machine (GOA-SVM) were developed. The results show that the spectral data need not be pre-processed, and the proposed hybrid wavelength selection strategy can further improve the identification ability of the model. Among the many models developed, ReliefF-SBS-GOA-SVM emerged as the most proficient classification model, yielding accuracy, sensitivity, and specificity rates of 94.44%, 95.23%, and 94.44%, respectively. This method not only holds promise for the discrimination of baijiu class attributes such as brand, origin, flavor, and vintage but also exhibits potential applicability in other non-targeted identification studies involving spectroscopy methodologies.

Linewidth measurement of external cavity lasers

Narrowband laser sources are used in applications that require high-precision or stable optical frequency. Such applications include high-resolution spectroscopy, long-distance measurement, and coherent optical communication. The linewidth of a laser is a direct measure of the laser's stability; therefore, characterization of laser linewidth is essential. In practice, however, determining a laser's linewidth is not a trivial task and typically requires expensive equipment or a complex experimental arrangement. This paper presents a straightforward, low-cost method based on unbalanced interferometry, which allows us to determine the visibility of fringe patterns as a function of the optical path difference and, consequently, the linewidth of the laser. As a test laser, we use a tunable external cavity laser source at around 780 nm, where an interference filter is employed for wavelength selection. Data obtained by applying the interferometric technique to this laser and the analysis of these data, along with the resulting linewidth value, are presented. Given that the described measurement setup is inexpensive, straightforward, and pedagogically accessible, it is well-suited for an instructional physics laboratory experiment and will also be of interest to laboratory researchers.

Potential of hyperspectral imaging for nondestructive determination of α-farnesene and conjugated trienol content in ‘Yali’ pear

The sesquiterpene α-farnesene and its corresponding oxidation products, namely conjugated trienols (CTols) is well known to be correlated with the development of superficial scald, a typical physiological disorder after a long term of cold storage in pear fruit. In this work, hyperspectral imaging (HSI) technology was used for nondestructive predicting of α-farnesene and CTols [CT258, CT281 and CT(281-290)] content in ‘Yali’ pear. In order to obtain the best performance of calibration model and simplify the calibration model further, various preprocessing methods together with their combinations and different wavelength selection algorithms, including successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE), were investigated and compared based on linear partial least square regression (PLSR) and nonlinear least square support vector machine (LS-SVM) models, respectively. In conclusion, compared to the PLSR models, the results of LS-SVM models based on original and preprocessing methods performed better for the prediction of α-farnesene and CTols, while the performance of LS-SVM models based on the selected characteristic wavelengths were worse. For α-farnesene, the best result was obtained by LS-SVM model based on MSC-FD pretreatment with the RPD value of 2.6, Rp = 0.925 and RMSEP = 4.387 nmol cm−2. And for CTols, CT281 performed better compared with CT258 and CT(281-290), achieving the result with RPD = 2.4, Rp = 0.913 and RMSEP = 2.734 nmol cm−2 based on LS-SVM model combined with SD pretreatment. The overall results illustrated HSI technology could be used for rapid and nondestructive prediction of α-farnesene and CTols in ‘Yali’ pear, which would be helpful for supporting postharvest decision systems.

Using Vis/NIR hyperspectral imaging and wavelength selection for accurate postharvest discrimination of loquat cultivars of similar appearance

Using Vis/NIR hyperspectral imaging and wavelength selection for accurate postharvest discrimination of loquat cultivars of similar appearance