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Non-Destructive Methodology to Determine Modulus of Elasticity in Static Bending of Quercus mongolica Using Near-Infrared Spectroscopy

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SENSORS 2018年第6期18卷 1963-1963页

作者： Liang, Hao Zhang, Meng Gao, Chao Zhao, Yandong Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Beijing Lab Urban & Rural Ecol Environm Beijing 100083 Peoples R China Key Lab State Forestry Adm Forestry Equipment & A Beijing 10083 Peoples R China

This article presents a non-destructive methodology to determine the modulus of elasticity (MOE) in static bending of wood through the use of near-infrared (NIR) spectroscopy. Wood specimens were obtained from Quercus mongolica growing in Northeast of China. The NIR spectra of specimens were acquired by using a one-chip NIR fiber optic spectrometer whose spectral range was 900 similar to 1900 nm. The raw spectra of specimens were pretreated by multiplication scatter correlation and Savitzky-Golay smoothing and differentiation filter. To reduce the dimensions of data and complexity of modeling, the synergy interval partial least squares and successive projections algorithm were applied to extract the characteristic wavelengths, which had closing relevance with the MOE of wood, and five characteristic wavelengths were selected from full 117 variables of a spectrum. Taking the characteristic wavelengths as input values, partial least square regression (PLSR) and the propagation neural network (BPNN) were implemented to establish calibration models. The predictive ability of the models was estimated by the coefficient of determination (r(p)) and the root mean square error of prediction (RMSEP) and in the prediction set. In comparison with the predicted results of the models, BPNN performed better results with the higher r(p) of 0.91 and lower RMSEP of 0.76. The results indicate that it is feasible to accurately determine the MOE of wood by using the NIR spectroscopy technique.

关键词： near-infrared spectroscopy the modulus of elasticity in static bending synergy interval partial least squares successive projections algorithm characteristic wavelengths

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Short wave-near infrared spectroscopy for predicting soluble solid content in intact mango with variable selection algorithms and chemometric model

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2024年 136卷

作者： Khatun, Sapna Al Masum, Abdullah Islam, Md Hamidul Ashik-E-Rabbani, Muhammad Rahman, Anisur Bangladesh Rice Res Inst BRRI Farm Machinery & Post Harvest Technol Div Gazipur 1701 Bangladesh Habiganj Agr Univ Dept Agr Mechanizat Habiganj 3300 Bangladesh Bangladesh Agr Univ Dept Farm Power & Machinery Mymensingh 2202 Bangladesh

Mango sweetness is one of the most prominent internal quality constituents that attract consumer's attention. The current common methods used to determine the sweetness of mango have significant disadvantages in that labor-intensive, time-consuming, and damaging techniques. This research aims to predict sweetness in mangoes using short wave-near infrared (SW-NIR) spectroscopy in the ranges 900-1650 nm, along with variable selection algorithms and chemometric model. A total of 120 mango samples were used to collect the spectra using a fibre module SW-NIR spectrometer. The partial least squares (PLS) regression with several spectral preprocessing methods was employed to develop the calibration model, and the best preprocessing technique was selected. The Savitzky-Golay second-derivative preprocessing technique performed better among the other preprocessing techniques with a correlation coefficient of prediction (r(pred)) of 0.74 and standard error of prediction (SEP) is 0.78 %Brix. After that, two variable selection techniques were used to select effective wavelength variables, including regression coefficient and successive projections algorithm (SPA). For SSC prediction in the range 900-1650 nm, the SPA-PLS model obtained a r(pred) of 0.78 and SEP of 0.67 %Brix. The current study unequivocally shows that the proposed SW-NIR spectroscopy coupled with a suitable chemometrics method can evaluate mango sweetness nondestructively.

关键词： Spectroscopy Mango Spectral pre-processing Partial least squares regression Variable selection techniques successive projections algorithm

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Qualitative and quantitative detection of microplastics in soil based on LIF technology combined with OOA-ELM/SPA-PLS

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MICROCHEMICAL JOURNAL 2024年 201卷

作者： Yan, Pengcheng Li, Guodong Wang, Wenchang Zhao, Yuting Wang, Jingbao Wen, Ziming Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232001 Peoples R China Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan 232001 Peoples R China Anhui Univ Sci & Technol Collaborat Innovat Ctr Mine Intelligent Equipment Huainan 232001 Peoples R China

Traditional methods for detecting soil microplastics include chemical digestion, density separation, staining, etc. These methods often only provide qualitative analysis of microplastics and are difficult to accurately quantify. To address this issue, laser induced fluorescence (LIF) technology is introduced, which can achieve high sensitivity, non-destructive, and rapid detection, and improve the accuracy and efficiency of qualitative and quantitative analysis of soil microplastics. Therefore, based on LIF technology, a qualitative prediction model for optimizing Extreme Learning Machine (ELM) using Osprey Optimization algorithm (OOA) and a quantitative prediction model combining Continuous Projection algorithm (SPA) with Partial Least Squares algorithm (PLS) were established. Qualitative analysis used eight types of microplastics, with AS, PA66, ABS, PS, POM, PBT, PET, and PVC as experimental samples. Initially, spectral data is obtained using LIF technology and the raw spectral data is pre-processed using polynomial smoothing algorithm (SG) and moving average (MA) methods. Subsequently, factor analysis (FA) and local linear embedding (LLE) techniques are applied to dimensionality reduction and then input into the ELM model. At the same time, the ELM parameter is optimized by using OOA algorithm. In the quantitative analysis, PA66 powder and soil are mixed in different proportions (10:0, 8:2, 5:5, 2:8, 0:10), SG and MA pre-processing are performed on the spectral data obtained by LIF technology, and SPA is used to select the spectral characteristic wavelength for the original data and the pre-processed spectral data, and then a linear regression model is established combined with PLS. In order to facilitate the comparison of data, the experimental results are reserved to 5 decimal places, the experimental results indicate that LIF technology is faster than traditional methods in extracting raw data. The convergence of the OOA-optimized ELM model is better than tha

关键词： Laser induced fluorescence technique Qualitative and quantitative detection Extreme learning machine Osprey optimization algorithm Partial least squares successive projections algorithm

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In-situ authentication of goat milk in terms of its adulteration with cow milk using a low-cost portable NIR spectrophotometer

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MICROCHEMICAL JOURNAL 2021年 163卷 105885-105885页

作者： dos Santos Pereira, Elainy Virginia de Sousa Fernandes, David Douglas Ugulino de Araujo, Mario Cesar Goncalves Dias Diniz, Paulo Henrique Sucupira Maciel, Maria Ines Univ Fed Paraiba Technol Ctr Food Sci & Technol Grad Program PPGCTA BR-58051900 Joao Pessoa Paraiba Brazil State Univ Paraiba CCT NUPEA BR-58429500 Campina Grande Paraiba Brazil Univ Fed Paraiba Dept Chem CCEN BR-58051970 Joao Pessoa Paraiba Brazil Fed Univ Western Bahia Pure & Appl Chem Grad Program BR-47810059 Barreiras BA Brazil Univ Fed Rural Pernambuco Food Sci & Technol Grad Program BR-52171900 Recife PE Brazil

This work aimed at the use of a miniaturized near-infrared (NIR) spectrophotometer for the in-situ goat milk authentication in terms of its adulteration by adding cow milk. For this, One-Class Partial Least Squares (OC-PLS), PLS for Discriminant Analysis (PLS-DA), and the successive projections algorithm for interval selection in PLS-DA (iSPA-PLS-DA) were employed. OC-PLS misclassified 1 pure goat milk sample as adulterated and 49 adulterated sample as pure in the test set, while PLS-DA misclassified 1 adulterated sample as pure goat milk and 2 pure goat milk samples as adulterated in the test set. On the other hand, the best predictive ability was achieved by iSPA-PLS-DA when using the spectra pre-processed with smoothing by 7-point window moving mean and baseline offset correction, classifying correctly 100% of the pure goat milk samples and misclassifying only one adulterated sample in the test set. This result reinforces the fact of the selection of intervals by SPA provided a more parsimonious model, with less latent variables than the other models, since it selects only the most relevant and interpretable chemical information. Therefore, the proposed methodology represents a promise, nondestructive, fast, and low-cost tool for screening the authentication of raw goat milk, also helping to support future decisions by the regulatory agencies to prevent this kind of fraud directly in the production local.

关键词： Dairy product Quality control Near-infrared spectroscopy successive projections algorithm Pattern recognition

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THE DEVELOPMENT OF PORTABLE DETECTOR FOR APPLES SOLUBLE SOLIDS CONTENT BASED ON VISIBLE AND NEAR INFRARED SPECTRUM

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INMATEH-AGRICULTURAL ENGINEERING 2020年第3期62卷 277-288页

作者： Peng, Fa Liu, ShuangXi Jiang, Hao Liu, XueMei Mu, JunLin Wang, JinXing Beijing Agr Equipment Res Ctr Beijing 100097 Peoples R China Shandong Agr Univ Coll Mech & Engn Tai An 271018 Shandong Peoples R China

In order to detect the soluble solids content of apples quickly and accurately, a portable apple soluble solids content detector based on USB2000 + micro spectrometer was developed. The instrument can communicate with computer terminal and mobile app through network port, Bluetooth and other ways, which can realize the rapid acquisition of apple spectral information. Firstly, the visible/near-infrared spectrum data and soluble solids content information of 160 apple samples were collected;secondly, the spectral data preprocessing methods were compared, and the results showed that the prediction model of sugar content based on partial least square (PLS) method after average smoothing preprocessing was accurate. The correlation coefficient (RP) and root mean square error (RMSEP) of the prediction model were 0.902 and 0.589 *Brix, respectively. Finally, on the basis of average smoothing preprocessing, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the wavelength of spectral data, and PLS model was constructed based on the selected 17 characteristic wavelengths, which can increase the accuracy of soluble solids content prediction model, increase the RP to 0.912, and reduce RMSEP to 0.511 *Brix. The portable visible/nearinfrared spectrum soluble solids prediction model based on the instrument and method has high accuracy, and the detector can quickly and accurately measure the soluble solids content of apple.

关键词： Apple soluble solids Visible/near infrared spectroscopy Portable Adaptive Reweighted algorithm successive projections algorithm

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A multispectral imaging system developed based on the spectral feature selection method for identification of housefly pupae

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MICROCHEMICAL JOURNAL 2024年 200卷

作者： Yang, Cheng-bo Li, Qing-zhi Tang, Feng Wu, Jing-jun Li, Bo Ye, Xin Yang, Li-ming China Acad Engn Phys Res Ctr Laser Fus Mianyang 621900 Peoples R China

Hyperspectral imaging systems can simultaneously obtain spectral and spatial information of fly pupae, especially achieving a large area of spectral information collection in a short period. However, hyperspectral imaging systems are expensive and require much computation. This study attempts to develop a four-channel spectral imaging system composed of a camera and four filters to achieve spectral discrimination of housefly pupae over a large area. It also aims to verify the feasibility of using spectral feature selection methods to guide the development of low-cost specialized spectral systems. Meanwhile, a strategy combining spectral degradation with the traversal method was proposed for determining the parameter indicators of optical components. Firstly, use a microspectrometer to acquire the spectra of fly pupae. Secondly, the successive projections algorithm (SPA) extracts four feature wavelengths related to fly pupa recognition from the full spectrum variables. Then, based on the strategy of spectral degradation combined with the traversal method, the parameters of the four filters are determined. Finally, a 4-channel spectral imaging system was constructed, and a prediction model was built with random forest (RF), while the spectral index (SI) method was used to improve model performance. The results indicate that this low-cost multispectral imaging system can achieve high-performance discrimination of housefly pupae (accuracy = 97.44 %). At the same time, SPA, spectral degradation, and traversal methods can provide effective central wavelength and resolution indicators for constructing low-cost multispectral imaging systems, offering a practical and feasible example to guide the development of specialized low-cost spectral devices using spectral feature selection methods.

关键词： Housefly pupae successive projections algorithm Spectral degradation Multispectral imaging system Spectral resolution

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The identification of aged-rice adulteration by support vector machine classification combined with characteristic wavelength variables

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MICROCHEMICAL JOURNAL 2024年 199卷

作者： Song, Jiahui Yu, Yue Wang, Ruoni Chen, Menting Li, Zhanming He, Xueming Ren, Zhongyang Dong, Hao Jiangsu Univ Sci & Technol Sch Grain Sci & Technol Zhenjiang 212100 Peoples R China Nanjing Univ Finance & Econ Coll Food Sci & Engn Nanjing 210023 Peoples R China Jimei Univ Coll Ocean Food & Biol Engn Xiamen 361021 Peoples R China Zhongkai Univ Agr & Engn Coll Light Ind & Food Sci Guangzhou 510225 Peoples R China

The aging of rice during long-term storage leads to a decline in quality and a decrease in consumption value. Therefore, the efficient and rapid detection of aged-rice adulteration is crucial for rice quality control. This paper aims to utilize near-infrared spectroscopy (NIRS) in conjunction with chemometrics to identify two types of rice adulteration: Late japonica rice and Southern japonica rice. To achieve this, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were utilized to select characteristic wavelengths from the spectral data, both with and without pretreatment methods. In order to establish a support vector machine classification (SVC) model with exceptional predictive capability, cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO) were adopted to find the best combination of parameters (C, g). Notably, the prediction accuracy achieved by MMN-CARS-CV-SVC, SNV-CARS-CV-SVC, SNV-CARS-GA-SVC, and SNV-CARS-PSO-SVC models was 98% for Southern japonica rice adulteration, with varying proportions of aging rice. Conversely, the prediction accuracy of the MSC-CARS-CV-SVC model reached 96% for Late japonica rice adulteration, with varying proportions of aged rice. In conclusion, the combination of NIRS and SVC enhances the discriminative analysis of aged-rice adulteration, and the outstanding generalization ability of the SVC model offers new possibilities for the analysis of adulteration in other cereal grains.

关键词： Adulteration successive projections algorithm Competitive adaptive reweighted sampling Chemometrics Support vector machine classification

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Optical Spectroscopy Sequential Wavelength Selection Using a Higher Leverage Approach

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2021年第6期5卷 1-4页

作者： Aquino, Bernardo Gupta, Vijay Howard, Scott Univ Notre Dame Dept Elect Engn Notre Dame IN 46556 USA

The problem of minimizing wavelength usage and, therefore, decreasing measurement time in spectroscopy experiments has been studied over the years, under many different scenarios. These solutions offer optimal or suboptimal wavelengths for systems with substances. In this letter, we propose a deterministic algorithm for wavelength selection for substance detection and classification of spectral data, generated by overdetermined systems, that is, when the number of wavelengths is greater than the number of substances. This type of problem is common when the increasing popular low signal-to-noise portable thermal sensors are used. A cardinality penalizing constrained multivariate regression is also proposed as the criterion for evaluating the mean-squared error. As a practical example, the spectrum of five different substances [i.e., 1) lignin, 2) cellulose, 3) petroleum jelly, 4) pentaerythritol tetranitrate, and 5) hexamine] were chosen as a proof of concept to the proposed method and compared with a similar method and were shown to achieve lower mean-squared error.

关键词： Mathematical model Wavelength measurement Minimization Sensors Linear regression Spectroscopy Optimization Sensor signal processing higher leverage spectroscopy successive projections algorithm variable selection

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An Ensemble successive Project algorithm for Liquor Detection Using Near Infrared Sensor

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SENSORS 2016年第1期16卷 89-89页

作者： Qu, Fangfang Ren, Dong Wang, Jihua Zhang, Zhong Lu, Na Meng, Lei Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Peoples R China Beijing Res Ctr Agr Stand & Testing Beijing 100097 Peoples R China

Spectral analysis technique based on near infrared (NIR) sensor is a powerful tool for complex information processing and high precision recognition, and it has been widely applied to quality analysis and online inspection of agricultural products. This paper proposes a new method to address the instability of small sample sizes in the successive projections algorithm (SPA) as well as the lack of association between selected variables and the analyte. The proposed method is an evaluated bootstrap ensemble SPA method (EBSPA) based on a variable evaluation index (EI) for variable selection, and is applied to the quantitative prediction of alcohol concentrations in liquor using NIR sensor. In the experiment, the proposed EBSPA with three kinds of modeling methods are established to test their performance. In addition, the proposed EBSPA combined with partial least square is compared with other state-of-the-art variable selection methods. The results show that the proposed method can solve the defects of SPA and it has the best generalization performance and stability. Furthermore, the physical meaning of the selected variables from the near infrared sensor data is clear, which can effectively reduce the variables and improve their prediction accuracy.

关键词： near infrared sensors information processing spectroscopy variable selection successive projections algorithm

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Confocal Raman spectroscopy assisted by chemometric tools: A green approach for classification and quantification of octyl p-methoxycinnamate in oil-in-water microemulsions

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MICROCHEMICAL JOURNAL 2023年第PartA期184卷

作者： Silva do Nascimento, Danielle Volpe, Veronica Fernandez, Cintia J. Oresti, Gerardo M. Ashton, Lorna Grunhut, Marcos Univ Nacl Sur Dept Chem INQUISUR UNS CONICET Bahia Blanca Buenos Aires Argentina Univ Nacl Sur Dept Biol Bioquim & Farm INIBIBB UNS CONICET Bahia Blanca Buenos Aires Argentina Univ Lancaster Dept Chem Lancaster England

This work proposes a green analytical method based on confocal Raman spectrometry and chemometrics tools for the qualitative and quantitative analysis of oil in water microemulsions loaded with the UVB filter octyl p-methoxycinnamate (OMC). The method does not use reagents and only 10 mu L of sample are needed. The analyzed microemulsion samples were synthetized in the laboratory using decaethylene glycol mono-dodecyl ether (21.9 %) as non-ionic surfactant, ethyl alcohol (7.3 %) as co-surfactant, oleic acid (1.5 %) as oil phase and water (69.3 %). A physicochemical characterization of the samples was carried out obtaining expected values for droplet size (<20 nm), polydispersity index (<0.290) and conductivity (0.04-0.07 mS cm-1), among others. Linear discriminant analysis (LDA) after selection of variables using the successive projections algorithm (SPA) and soft independent modelling of class analogy (SIMCA) were employed to classify microemulsions with different concentrations of OMC (1.0 to 10.0 %). In the case of LDA, seven Raman spectral variables were previously selected by SPA and after this SPA-LDA model resulted in one error in the prediction set achieving an accuracy of 97.8 %. The SIMCA model (alpha = 0.05) presented an explained variance higher 97 % using four principal com-ponents and it allowed the correct classification of 100 % of the samples (N = 15). In the quantitative analysis, partial least squares (PLS) was used to determine OMC in a range according to international legislation. The model presented optimal statistical parameters (R2 = 0.9699;RMSEP = 0.54 %) and the prediction of samples were in close agreement with HPLC method. Moreover, the greenery of the method was estimated using the AGREE metric and an optimal value of 0.85 was obtained demonstrating the proposed analytical method results environmentally friendly.

关键词： UV filters Octyl p-methoxycinnamate Microemulsions Confocal Raman Spectroscopy Linear Discriminant Analysis successive projections algorithm Soft Independent Modelling by Class Analogy Partial Least Squares Green Analytical Chemistry

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