- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Detection of Knot Defects on Coniferous Wood Surface Using Near Infrared Spectroscopy and Chemometrics
摘要： Lumber pieces usually contain defects such as knots, which strongly affect the strength and stiffness. To develop a model for rapid, accurate grading of lumbers based on knots, Douglas fir, spruce-pine-fir (SPF), Chinese hemlock, and Dragon spruce were used. The experiments explored the effects of modelling methods and spectral preprocess methods for knot detection, and investigated the feasibility of using a model built within one species to discriminate the samples from other species, using a novel variable selection method-random frog to select effective wavelengths. The results showed that least squares-support vector machines coupled with first derivative preprocessed spectra achieved best performance for both single and mixed models. Models built within Dragon spruce could be used to classify knot samples from SPF and Chinese hemlock but not Douglas fir, and vice versa. Eight effective wavelengths (1314 nm, 1358 nm, 1409 nm, 1340 nm, 1260 nm, 1586 nm, 1288 nm, and 1402 nm) were selected by RF to build effective wavelengths based models. The sensitivity, specificity, and accuracy in the validation set were 98.49%, 93.42%, and 96.30%, respectively. Good results could be obtained when using data at just eight wavelengths, as an alternative to evaluating the whole spectrum.
关键词： Coniferous wood,Knot detection,Near infrared spectroscopy (NIRS),Random frog algorithm,Least squares-support vector machines (LS-SVM)
更新于2025-09-23 15:23:52
-
Use of near infrared spectroscopy and chemometrics to evaluate the shelf-life of cloudy sonicated apple juice
摘要： Fresh products, such as cloudy apple juice, could be preserved from early spoilage through the application of non-thermal processes such as sonication. However, shelf-life analyses based on microbiological and sensory evaluations are expensive and time consuming. Few studies have applied near infrared spectroscopy to evaluate the quality and decay of apple juices. Here, a feasibility trial was conducted to study the spectral behaviour at 1300–2500 nm combined with chemometric approaches. The shelf-life was monitored during two experiments, a challenge test with juices inoculated with spoilage yeasts (inoculated non-sonicated (INS)) and then submitted to sonication treatments (inoculated sonicated (IS)), and a storage test to evaluate the spoilage on non-inoculated juices (non-inoculated non-sonicated (NINS)) and sonicated non-inoculated juices (non-inoculated sonicated (NIS)). These experiments were investigated at six different refrigeration times 7, 14, 21, 28 and 60 days. Two functions were modelled to describe the behaviours of the first principal component according to the storage time. In agreement with a previous chemical and sensory evaluation, this approach allowed us to highlight shelf-life end points of 7 and 14 days for non-sonicated and sonicated samples, respectively. Three different models were evaluated for classification purposes: (1) sonicated versus non-treated samples, (2) end-point shelf-life evaluation at seven days for the NINS and INS juices and (3) end-point shelf-life discrimination at 14 days for IS and NIS samples. A partial least square-discriminant analysis enabled a group classification with accuracy values ranging from 0.63 to 1.00. The application of a variable importance in projection index to interpret the wavelengths of the spectral features suggests a contribution of organic acids and lipids to the prediction of decay. A canonical discriminant analysis provided a clearer separation of samples according to the storage time, especially in relation to the two time thresholds of 7 and 14 days.
关键词： Apple juice,spoilage,near infrared spectroscopy,shelf-life,sonication
更新于2025-09-23 15:23:52
-
A variable importance criterion for variable selection in near-infrared spectral analysis
摘要： Variable selection is a universal problem in building multivariate calibration models, such as quantitative structure-activity relationship (QSAR) and quantitative relationships between quantity or property and spectral data. Significant improvement in the prediction ability of the models can be achieved by reducing the bias induced by the uninformative variables. A new criterion, named as C, is proposed in this study to evaluate the importance of the variables in a model. The value of C is defined as the average contribution of a variable to the model, which is calculated by the statistics of the models built with different combinations of the variables. In the calculation, a large number of partial least squares (PLS) models are built using a subset of variables selected by randomly re-sampling. Then, a vector of the prediction errors, in terms of root mean squared error of cross validation (RMSECV), and a matrix composed of 1 and 0 indicating the selected and unselected variables can be obtained. If multiple linear regression (MLR) is employed to model the relationship between the RMSECVs and the matrix, the coefficients of the MLR model can be used as a criterion to evaluate the contribution of a variable to the RMSECV. To enhance the efficiency of the method, a multi-step shrinkage strategy was used. Comparison with Monte Carlo-uninformative variables elimination (MC-UVE), randomization test (RT) and competitive adaptive reweighted sampling (CARS) was conducted using three NIR benchmark datasets. The results show that the proposed criterion is effective for selecting the informative variables from the spectra to improve the prediction ability of models.
关键词： multivariate calibration,multi-step strategy,variable selection,near-infrared spectroscopy
更新于2025-09-23 15:23:52
-
Simultaneous Determination of Clarithromycin, Tinidazole and Omeprazole in Helicure Tablets Using Reflectance Near-Infrared Spectroscopy with the Aid of Chemometry
摘要： A near infrared spectroscopic method for the simultaneous determination of the active principles clarithromycin, tinidazole and omeprazole in a pharmaceutical preparation was developed. The three active principles are quantified using partial least-squares regression methods. The proposed method is applicable over a wide analyte concentration range (80–120%) of labeled content, so it requires careful selection of the calibration set and to ensure thorough homogenization of the product. The method was validated in accordance with the ICH standard validation guidelines for NIR spectroscopy by determining its selectivity, linearity, accuracy, precision and stability. Based on the results, it is an effective alternative to the existing choice (HPLC) for the same purpose.
关键词： Partial least squares,Clarithromycin,Helicure,Near Infrared Spectroscopy,Preprocessing,Genetic algorithm,Multivariate calibration
更新于2025-09-23 15:23:52
-
Nondestructive Detection of Postharvest Quality of Cherry Tomatoes Using a Portable NIR Spectrometer and Chemometric Algorithms
摘要： The aim of this study was to assess the applicability of a portable NIR spectroscopy system and chemometric algorithms in intelligently detecting postharvest quality of cherry tomatoes. The postharvest quality of cherry tomatoes was evaluated in terms of firmness, soluble solids content (SSC), and pH, and a portable NIR spectrometer (950–1650 nm) was used to obtain the spectra of cherry tomatoes. Partial least square (PLS), support vector machine (SVM), and extreme learning machine (ELM) were applied to predict the postharvest quality of cherry tomatoes from their spectra. The effects of different preprocessing techniques, including Savitzky–Golay (S-G), multiplicative scattering correction (MSC), and standard normal variate (SNV) on prediction performance were also evaluated. Firmness, SSC and pH values of cherry tomatoes decreased during storage period, based on which the tomato samples could be classified into two distinct clusters. Similarly, cherry tomatoes with different storage time could also be separated by the NIR spectroscopic characteristics. The best prediction accuracy was obtained from ELM algorithms using the raw spectra with Rp2, RMSEP, and RPD values of 0.9666, 0.3141 N, and 5.6118 for firmness; 0.9179, 0.1485%, and 3.6249 for SSC; and 0.8519, 0.0164, and 2.7407 for pH, respectively. Excellent predictions for firmness and SSC (RPD value greater than 3.0), good prediction for pH (RPD value between 2.5 and 3.0) were obtained using ELM model. NIR spectroscopy is capable of intelligently detecting postharvest quality of cherry tomatoes during storage.
关键词： Partial least square,Extreme learning machine,Support vector machine,Cherry tomato,Near infrared spectroscopy
更新于2025-09-23 15:23:52
-
Rapid, non-destructive determination of ginseng seed moisture content by near infrared spectroscopy technology
摘要： Ginseng seed moisture content (SMC) determination and monitoring are of great importance during seed storage and in trading. The traditional oven-drying method for SMC measurement is accurate but takes both time and labour. The objective of this study was to develop a rapid and non-destructive method for ginseng SMC determination using near infrared (NIR) spectroscopy. Eighteen freshly harvested seed lots stored for different periods (days) were used for NIR model development and 12 commercial seed lots were used for validation of the model. The model developed in the present work had an R2 of 0.9913, residual prediction deviation (RPD) of 11.3 and low root mean square errors assessed by cross-validation (RMSECV; 0.387%). For commercial seed lot measurement, the predicted values of SMC were nearly the same as measured ones, with the relative differences less than 2.96%. In conclusion, NIR spectroscopy suitable for rapid and nondestructive determination of ginseng SMC.
关键词： near infrared spectroscopy,seed moisture content,ginseng
更新于2025-09-23 15:23:52
-
Noise reduction for near-infrared spectroscopy data using extreme learning machines
摘要： The near infrared (NIR) spectra technique is an effective approach to predict chemical properties and it is typically applied in petrochemical, agricultural, medical, and environmental sectors. NIR spectra are usually of very high dimensions and contain huge amounts of information. Most of the information is irrelevant to the target problem and some is simply noise. Thus, it is not an easy task to discover the relationship between NIR spectra and the predictive variable. However, this kind of regression analysis is one of the main topics of machine learning. Thus machine learning techniques play a key role in NIR based analytical approaches. Pre-processing of NIR spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena (noise) in the spectra in order to improve the regression or classification model. In this work, we propose to reduce the noise using extreme learning machines which have shown good predictive performances in regression applications as well as in large dataset classification tasks. For this, we use a novel algorithm called C-PL-ELM, which has an architecture in parallel based on a non-linear layer in parallel with another non-linear layer. Using the soft margin loss function concept, we incorporate two Lagrange multipliers with the objective of including the noise of spectral data. Six real-life dataset were analyzed to illustrate the performance of the developed models. The results for regression and classification problems confirm the advantages of using the proposed method in terms of root mean square error and accuracy.
关键词： Parallel layers,Constrained optimization,Regression,Near-infrared spectroscopy,Classification
更新于2025-09-23 15:23:52
-
Nondestructive egg freshness assessment from the equatorial and blunt region based on visible near infrared spectroscopy
摘要： This research was to study which orientation was better for freshness prediction of the white-shelled eggs using visible near infrared spectroscopy. The transmission spectra were acquired in the equatorial region and at the blunt end of the eggs. After each spectral measurement, the Haugh unit, yolk index, and albumen pH as the freshness parameters were simultaneously measured using traditional destructive methods. Pretreatment methods containing Savitzky-Golay smoothing, multiplicative scatter correction, the standard normal variate, the first derivative and the second derivative were used. A partial least squares regression was developed to predict the Haugh unit, yolk index, and albumen pH. The best correlation coefficients of prediction were obtained from the equatorial region, and were 0.881, 0.855, and 0.888 for the Haugh unit, yolk index and albumen pH, respectively. And root mean square errors in the prediction set were 7.720, 0.034, and 0.147 for the Haugh unit, yolk index and albumen pH, respectively. The results illustrated that the equatorial region showed better ability than the blunt end to predict freshness of the white-shelled eggs.
关键词： nondestructive,visible near infrared spectroscopy,orientation,freshness,Egg
更新于2025-09-23 15:22:29
-
[IEEE 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Stuttgart, Germany (2018.11.20-2018.11.22)] 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Hybrid sEMG, NIRS and MMG Sensor System
摘要： In recent years, surface electromyography (sEMG) is widely used in human-computer interface (HCI). For example, it is used for prosthetic manipulation to improve the quality of amputees' life. However, sEMG approaches has some drawbacks such as poor robustness of the electrode-skin interface. Near-infrared spectroscopy (NIRS) and mechanomyography (MMG) can also monitor muscle motion. Comparing with approaches that only use sEMG signal, hybrid sEMG, NIRS and MMG sensor system will have better system performance. Investigations about the fusion of sEMG, NIRS and MMG are scant. This paper presents a hybrid sEMG, NIRS and MMG sensor system and puts it into practice. Fuse surface electromyography, near-infrared spectroscopy and mechanomyography acquisition circuits into a compact sensor, which can measure the muscle motion from the modalities of electrophysiology, optics and acoustics. Using the hybrid sensor system, incremental grip force experiment is carried out to explore the relationship between the three signals, blood oxygen metabolism and grip force. And muscle fatigue is carried out in order to explain the phenomenon of muscle fatigue from the perspective of electrophysiology, blood oxygen metabolism and mechanomyography.
关键词： Near-infrared Spectroscopy,Surface Electromyography,Mechanomyography,Hybrid Sensor
更新于2025-09-23 15:22:29
-
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