- 标题
- 摘要
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- 实验方案
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Effect of cultivar and season on the robustness of PLS models for soluble solid content prediction in apricots using FT-NIRS
摘要： FT-NIR models were developed for the non-destructive prediction of soluble solid content (SSC), titratable acidity (TA), firmness and weight of two commercially important apricot cultivars, "Hac?halilo?lu" and "Kabaas?" from Turkey. The models constructed for SSC prediction gave good results. We could also establish a model which can be used for rough estimation of the apricot weight. However, it could not be possible to predict accurately TA and firmness of the apricots with FT-NIR spectroscopy. The study was further extended over 3 years for the SSC prediction. Validation of the both mono and multi-cultivar models showed that model performances may exhibit important variations across different harvest seasons. The robustness of the models was improved when the data of two or three seasons were used. It was concluded that in order to developed reliable SSC prediction models for apricots the spectral data should be collected over several harvest seasons.
关键词： Prunus armeniaca L.,FT-NIR,Soluble solid content,PLS-R
更新于2025-09-23 15:23:52
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[ACM Press the 2017 International Conference - Singapore, Singapore (2017.12.27-2017.12.29)] Proceedings of the 2017 International Conference on Information Technology - ICIT 2017 - The Detection of Nitrite Content in Bacon based on Hyperspectral Technique
摘要： As a common food additive, nitrite has been widely used in meet products such as bacon. However, when the content of nitrite in food is overproof, consumer’s health will be seriously endangered. To solve this problem, this paper takes bacon as the sample to research the feasibility of hyperspectral technique in nitrite fast detecting. The partial least- squares (PLS) is utilized to associate the nitrite content data obtained via hyperspectral technique with the data obtained via GB method, and then the nitrite content calculating model is built. After comparing several models that adopt different spectrum pre-processing methods, the research team find that the first-order derivative plus vector normalization model is the best, whose RMSECV is 0.251 and r2 is 0.972. The result proves that hyperspectral can be effectively used in nitrite content prediction.
关键词： Chinese bacon,PLS,Hyperspectral,Nitrite
更新于2025-09-23 15:23:52
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Potential of Near-infrared Spectroscopy to Detect Defects on the Surface of Solid Wood Boards
摘要： Defects on the surface of solid wood boards directly affect their mechanical properties and product grades. This study investigated the use of near-infrared spectroscopy (NIRS) to detect and classify defects on the surface of solid wood boards. Pinus koraiensis was selected as the raw material. The experiments focused on the ability to use the model to sort defects on the surface of wood into four types, namely live knots, dead knots, cracks, and defect-free. The test data consisted of 360 NIR absorption spectra of the defect samples using a portable NIR spectrometer, with the wavelength range of 900 to 1900 nm. Three pre-processing methods were used to compare the effects of noise elimination in the original absorption spectra. The NIR discrimination models were developed based on partial least squares and discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM), and back-propagation neural network (BPNN) from 900 to approximately 1900 nm. The results demonstrated that the BPNN model exhibited the highest classification accuracy of 97.92% for the model calibration and 97.50% for the prediction set. These results suggest that there is potential for the NIR method to detect defects and differentiate between types of defects on the surface of solid wood boards.
关键词： Surface defects,BPNN,PLS - DA,LS-SVM,Near-infrared spectroscopy,Solid wood boards
更新于2025-09-23 15:22:29
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[IEEE 2018 37th Chinese Control Conference (CCC) - Wuhan (2018.7.25-2018.7.27)] 2018 37th Chinese Control Conference (CCC) - On-line Detection and Analysis of Alloy Steel Elements Based on the LIBS Technology and Random Forest Regression
摘要： The Laser Induced Breakdown Spectroscopy (LIBS) technology can be used to detect the elements in the alloy steel in real time. Quantitative analysis method of the traditional LIBS technology mainly has the calibration method and calibration free method, but there are two shortcomings: low prediction accuracy and over fitting. Random Forest Regression (RFR) algorithm can be used for classification and regression, can effectively avoid 'overfitting' phenomenon. Therefore, in this paper, we combine the random forest regression algorithm with laser induced breakdown spectroscopy applied to the detection of the concentration of alloy steel elements in the metallurgy industry. At the same time, compared with partial least squares method based on the LIBS, the results show that the random forest algorithm combined with the LIBS technology has the higher prediction accuracy, lower root mean square error and better robustness.
关键词： PLS,root mean square error,quantitative analysis,LIBS,RFR
更新于2025-09-23 15:22:29
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Surface-enhanced Raman scattering method for the identification of methicillin-resistant Staphylococcus aureus using positively charged silver nanoparticles
摘要： The article describes a SERS-based method for diagnosis of bacterial infections. Positively charged silver nanoparticles (AgNPs+) were employed for identification of methicillin-resistant Staphylococcus aureus (MRSA). It is found that AgNPs+ undergo self-assembly on the surface of bacteria via electrostatic aggregation. The assembled AgNPs+ are excellent SERS substrates. To prove the capability of SERS to differentiate between S. aureus and other microorganisms, six standard strains including S. aureus 29213, S. aureus 25923, C. albicans, B. cereus, E. coli, and P. aeruginosa were tested. To further demonstrate its applicability for the identification of MRSA in clinical samples, 52 methicillin-sensitive S. aureus (MSSA) isolates and 215 MRSA isolates were detected by SERS. The total measurement time (include incubation) is 45 min when using a 3 μL sample. The method gives a strongly enhanced Raman signal (at 730 cm?1 and 1325 cm?1) with good reproducibility and repeatability. It was successfully applied to the discrimination of the six strain microorganisms. The typical Raman peaks of S. aureus at 730, 1154, 1325, and 1457 cm?1 were observed, which were assigned to the bacterial cell wall components (730 cm?1- adenine, glycosidic ring mode, 1154 cm?1- unsaturated fatty acid, 1325 cm?1- adenine, polyadenine, and 1457 cm?1 for -COO- stretching). S. aureus was completely separated from other species by partial least squares discriminant analysis (PLS-DA). Moreover, 52 MSSA isolates and 215 MRSA isolates from clinical samples were identified by PLS-DA. The accuracy was almost 100% when compared to the standard broth microdilution method. A classification based on latent structure discriminant analysis provided spectral variability directly. Conceivably, the method offers a potent tool for the identification of bacteria and antibiotics resistance, and for studies on antibiotic-resistance in general.
关键词： S. aureus,Nanoparticles,Methicillin resistance,Antibiotics,Latent structure discriminant analysis classification (OPLS-DA),SERS,Partial least squares discriminant analysis (PLS-DA),AgNPs,Discriminant analysis,Raman spectroscopy
更新于2025-09-23 15:22:29
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Spectroscopy approach to methanol detection in waste fat methyl esters
摘要： Second-generation biodiesel manufactured from waste cooking oils (WCO) and inedible animal fats (AF) are one of the alternatives to the first generation (1G) vegetable oil-based biodiesel. In this study, a quality control method is proposed to evaluate methanol content in waste fat methyl esters and is based on near infrared spectroscopy (NIR) combined with multivariate analysis. More specifically, calibration models are constructed using partial least squares regression (PLS) for the prediction of methanol content in rapeseed oil methyl ester (ROME), waste cooking oil methyl ester (WCOME), chicken fat methyl ester (CFME) and pork fat methyl ester (PFME) by Vis-NIR spectrometer. The calibration models are based on the absorbance spectra and computed data from five wavelength regions of 400–2170 nm, 780–2170 nm, 1400–2170 nm, 1400–1600 nm and 1970–2170 nm. For the cases with the highest prediction ability obtained in this study, the coefficient of determination of the model's goodness-of-fit for methanol concentrations range 0–5% (v/v) was R2 N 0.990, and for concentrations 0–1% (v/v) was R2 N 0.994, indicating the spectroscopic approach effectiveness in methanol content detection relevant to the biofuel quality assessment. A pseudo-univariate limits of detection (LODpu) and quantification (LOQpu) as well as ratio of performance to deviation (RPD) were used to confirm the validity and to evaluate the practical applicability of developed models. In addition, the obtained results indicate the possibility of developing a transmission sensor for online monitoring of the production process and the quality of biofuel.
关键词： PLS calibration models,Waste cooking oil,Animal fat biofuel,Vis-NIR spectroscopy
更新于2025-09-23 15:21:21
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Vitamin C Determination by Ultraviolet Spectroscopy and Multiproduct Calibration
摘要： In this work the vitamin C was determined in industrialized nectar juices through ultraviolet (UV) spectroscopy and multiproduct multivariate calibration, based on partial least squares (PLS) regression. Since samples with different flavors, sugar content (light or not) were together in the model construction, it can be considered as a multiproduct and, due to the heterogeneity of the samples, it was necessary to optimize the calibration and validation sets by outliers elimination. The model was developed and validated by the evaluation of the figures of merit such as: accuracy, sensitivity, analytical sensitivity, adjust, linearity, relative prediction deviation, limits of detection and quantification, indicating that the multiproduct model developed from UV spectroscopy and PLS regression can be used in the industrial routine analysis as an alternative to titration or other time and reagent consuming methods. Here, it was evidenced that the UV-PLS multiproduct model provides advantages as being free of sample preparation steps, is suitable to be updated in order to measure other parameters, does not generates residues and is feasible to be implemented for on-line monitoring. Furthermore, the application of multivariate calibration in multiproduct models is extremely attractive from the industrial point of view.
关键词： multiproduct calibration,vitamin C,PLS,UV spectroscopy
更新于2025-09-23 15:21:21
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Construction of global and robust near-infrared calibration models based on hybrid calibration sets using Partial Least Squares (PLS) regression
摘要： Near-infrared spectroscopy (NIR) models built on a particular instrument are often invalid on other instruments due to spectral inconsistencies between the instruments. In the present work, global and robust NIR calibration models were constructed by partial least square (PLS) regression based on hybrid calibration sets, which are composed of both primary and secondary spectra. Three datasets were used as case studies. The first consisted of 72 radix scutellaria samples measured on two NIR spectrometers with known baicalin content. The second was composed of 80 corn samples measured on two instruments with known moisture, oil, and protein concentrations. The third dataset included 279 primary samples of tobacco with known nicotine content and 78 secondary samples of tobacco with known nicotine concentrations. The effect of the number of secondary spectra in the hybrid calibration sets and the methods for selecting secondary spectra on the PLS model performance were investigated by comparing the results obtained from different calibration sets. This study shows that the global and robust calibration models accurately predicted both primary and secondary samples as long as the ratios of the number of primary spectra to the number of secondary spectra were less than 22. The models performance was not influenced by the selection method of the secondary spectra. The hybrid calibration sets included the primary spectral information and also the secondary spectra; rendering the constructed global and robust models applicable to both primary and secondary instruments.
关键词： global and robust models,hybrid calibration set,Near-infrared spectroscopy,partial least squares (PLS) regression
更新于2025-09-23 15:21:01
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Authentication of Grappa (Italian grape marc spirit) by Mid and Near Infrared spectroscopies coupled with chemometrics
摘要： The aim of the present study is to authenticate Grappa spirits and to develop a non-destructive methodology which would allow detecting possible adulteration (by less valuable spirits) on this product. Grappa is an Italian alcoholic drink obtained by distillation of grape marks which has recently received the Geographical Indication (GI) label. As a high added-value product, it is relevant to develop methodologies which allow its authentication and detecting possible frauds (e.g., adulterations); and, whether feasible, it would be suitable to achieve these goals through non-destructive approaches (in order to minimize the economic loss). Mid Infrared (MIR) and Near Infrared (NIR) spectroscopies have been used for the authentication and the characterization of the spirits under investigation. The present work is conceptually divided into two parts: the first one, centered on the authentication of grappa spirits, focused on distinguishing them from other Italian distillates, and a second one aimed at developing an analytical methodology suitable to discern between pure and adulterated grappas. Both classification problems have been investigated by PLS-DA and by three multi-block strategies, i.e., Multi-Block Partial Least Squares (MB-PLS), Sequential and Orthogonalized Partial Least Squares (SO-PLS) and Sequential and Orthogonalized Covariance Selection (SO-CovSel) in order to test whether a data-fusion approach would lead to an improvements of the classification rates. The best results (in terms of predictions) were provided by multi-block strategies; in particular, they provided 100 % of correct classification when applied to discriminate pure and adulterated samples, suggesting these methodologies are definitely suitable for the proposed purpose.
关键词： Adulteration,IR,SO-CovSel,SO-PLS,Data fusion,PLS-DA
更新于2025-09-23 15:19:57
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A feasibility quantitative analysis of NIR spectroscopy coupled Si-PLS to predict coco-peat available nitrogen from rapid measurements
摘要： Available nitrogen was an important index to evaluate the supply capacity of nitrogen fertilizer in planting environment. The study of coco-peat available nitrogen content rapid detection technology was of great significance for instructing scientific fertilization. In this study, near infrared (NIR) spectroscopy was used to realize rapid quantitative detection of available nitrogen in coco-peat. Seven different spectral pretreatment methods were adopted to pre-process spectral data with detection band range of 1000–2500 nm (full-band), and synergy interval partial least squares (Si-PLS) was used to screen the optimum combination sub-intervals reflecting coco-peat available nitrogen content from the original full-band spectral data and various pre-treated spectral data. The spectral prediction models of coco-peat available nitrogen based on full-band spectral data and optimal combined sub-intervals spectral data were respectively established, the improvement effects of different pretreatment methods on the accuracy of coco-peat available nitrogen spectral prediction models were analyzed, and the performances of the optimal full-band spectral prediction model and combined sub-intervals prediction model were compared. By analysis and comparison, the first derivative combined with Savitzky-Golay (S-G) smoothing was used to pre-process spectral data, Si-PLS was used to screen the spectral data of 1724–1784 nm, 1852–1922 nm, 1923–1999 nm and 2175–2272 nm, and then the optimal spectral prediction model of available nitrogen content in coco-peat could be established by using the four bands spectral data. For the optimal model, the correlation coefficient and root mean square error of calibration set were 0.994 and 6.998 mg/100 g respectively, the correlation coefficient and root mean square error of prediction set were 0.993 and 7.390 mg/100 g respectively, and the RPD was 8.062. It showed that the combination of NIR spectroscopy and Si-PLS could realize coco-peat available nitrogen quickly and accurately quantitative detection, Si-PLS could effectively reduce the input variables of the established model and simplify the complexity of the model. It also provided a reference for development of a coco-peat available nitrogen content rapid detection device based on characteristic band sub-intervals spectral data.
关键词： Full-band,Si-PLS,Coco-peat,Sub-interval,Available nitrogen
更新于2025-09-23 15:19:57