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Qualitative and quantitative analysis of counterfeit Fluconazole capsules: A non-invasive approach using NIR spectroscopy and chemometrics
摘要： The ultimate goal of the study is to present a method of authentication of hard-shell capsules of medicines packed in polyvinylchloride (PVC) blisters without damaging the primary packaging. This is done by collecting NIR spectra in a non-invasive mode and subsequent analysis of measurements by a one-class classification procedure. The first part of the study demonstrates that NIR spectra collected through a PVC blister and capsule shell do carry information about the medication itself. Firstly, this is done by visual inspection of spectra of the sample, its interfering layers and main pharmaceutical ingredients. Secondly, three regression models for quantification of active pharmaceutical ingredient (API) are built. The possibility of calibration and prediction of API through several nuisance layers using NIR spectroscopy is demonstrated. In order to solve the authentication problem the data driven soft Independent modeling of class analogies method is applied to the collected NIR spectra. The constructed model is validated using laboratory prepared mixtures. Afterwards, the model was applied to real counterfeited samples. Capsules of fluconazole are used for demonstration of the proposed approach.
关键词： counterfeit medicine,non-invasive measurements,NIR spectroscopy,fluconazole,chemometric analysis
更新于2025-09-10 09:29:36
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Application of visible-near infrared spectroscopy to evaluate the quality of button mushrooms
摘要： The Agaricus bisporus mushroom is one of the most cultivated and consumed mushrooms in the world, thanks to its delicacy, nutritional value and flavour. The quality evaluation of the A. bisporus during the harvest is generally established by a visual check by trained operators. This method complies with the request of the Distribution Channel (DC) to retailers and guarantees very low physical damage to the mushrooms; nevertheless, it is subjective and it does not guarantee the highest quality standard for the consumer. The aim of this study was to test the use of visible/near infrared (vis/NIR) reflectance spectroscopy (400–1000 nm) to objectively evaluate the quality parameters of A. bisporus mushrooms. A total of 167 samples of A. bisporus mushrooms were harvested according to the main DC purchasing standards. The vis/NIR analyses were performed the day of sampling just before the physico-chemical analyses (sizes, firmness, soluble solids content and moisture content) used as reference quality parameters. The vis/NIR spectra were correlated to reference measures in order to build predictive models using the partial least squares regression method. Calculated models gave positive results regarding the prediction of the moisture content (r2 (pred) ? 0.78) and firmness (r2 (pred) ? 0.78). Results of this explorative study could be considered encouraging and demonstrate the applicability of vis/NIR spectroscopy on A. bisporus as a rapid technique (i) to monitor the productive process directly at the company, (ii) to standardize the harvest moment, and (iii) to support DC’s buyers’ choices, nowadays exclusively based on product external characteristics.
关键词： quality,vis/NIR spectroscopy,harvest,reflectance,mushrooms,distribution channel
更新于2025-09-10 09:29:36
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EXPRESS: Bulk Protein and Oil Prediction in Soybean Using Transmission Raman Spectroscopy: A Comparison of Approaches to Optimize Accuracy
摘要： Rapid measurements of protein and oil content are important for a variety of uses, from sorting of soybeans at the point of harvest to feedback during soybean meal production. In this study, our goal is to develop a simple protocol to permit rapid and robust quantitative prediction of soybean constituents using transmission Raman spectroscopy (TRS). To develop this approach, we systematically varied the various elements of the measurement process to provide a diverse test bed. First, we utilized an in-house-built benchtop TRS instrument such that suitable optical configurations could be rapidly deployed and analyzed for experimental data collection for individual soybean grains. Second, we also utilized three different soybean varieties with relatively low (33.97%), medium (36.98%), and high protein (41.23%) contents to test the development process. Third, samples from each variety were prepared using whole bean and three different sample treatments (i.e., ground bean, whole meal, and ground meal). In each case, we modeled the data obtained using partial least squares (PLS) regression and assessed spectral metric-based multiple linear regression (metric-MLR) approaches to build robust prediction models. The metric-MLR models showed lower root mean square errors (RMSEPs), and hence better prediction, compared to corresponding classical PLS regression models for both bulk protein and oil for all treatment types. Comparing different sample preparation approaches, a lower RMSEPs was observed for whole meal treatment and thus the metric-MLR modeling with ground meal treatment was considered to be optimal protocol for bulk protein and oil prediction in soybean, with RMSEP values of 1.15±0.04 (R2= 0.87) and 0.80±0.02 (R2= 0.87) for bulk protein and oil, respectively. These predictions were nearly two- to three-fold better (i.e., lower RMSEPs) than the corresponding NIR spectroscopy measurements (i.e., secondary gold standards in grain industry). For content prediction in whole soybean, incorporating physical attributes of individual grains in metric-MLR approach show up to 22% improvement in bulk protein and a relatively mild (up to ~ 5%) improvement in bulk oil prediction. The unique combination of metric-MLR modeling approach (which is rare in the field of grain analysis) and sample treatments resulted in improved prediction models, and using the physical attributes of individual grains is suggested as a novel measure for improving accuracy in prediction.
关键词： near-infrared spectroscopy,Soybean,NIR spectroscopy,MLR,transmission Raman spectroscopy,multiple liner regression,PLS regression
更新于2025-09-09 09:28:46
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Reflectance spectroscopy of ammonium-bearing phyllosilicates
摘要： The identification of NH4-bearing phyllosilicates on Ceres poses the question on the NH4-carrier phase(s) and in this study we describe the laboratory production and IR spectroscopic measurements of a suite of ten NH4-phyllosilicates, starting from the corresponding NH4-free minerals. For each mineral, we prepared three types of powder samples: raw (R), ammoniated (A), and leached (L). All samples have been spectrally characterized by means of visible/infrared spectroscopy in the INAF-IAPS laboratories with the FieldSpec Pro in the 0.35-2.5 μm range, and with the FT-IR, using a Vertex 80 spectrometer operating in the range of 2 to 14 μm. The samples were also measured with the SPectral IMager, an imaging spectrometer operating in the spectral range 0.2 – 5.1 μm, which is a replica of the VIR spectrometer on-board Dawn spacecraft. Reflectance spectra of the ammoniated clays show bands near 1.56 μm, 2.05 μm, 2.12 μm, 3.06 μm, 3.25 μm, 3.55 μm, 4.2 μm, 5.7 μm and 7.0 μm that are related to the presence of nitrogen complexes. Treatment of phyllosilicates with ammonia shows that different minerals behave in different ways: NH4+ ions are easily accepted by the smectites, while other non-expandable structures do not accept these ions. The obtained results can be used to better constrain the NH4-bearing species present on Ceres and, possibly, other bodies of the solar system.
关键词： Reflectance spectroscopy,smectites,VIS-NIR spectroscopy,Ceres,ammonium-bearing phyllosilicates
更新于2025-09-09 09:28:46
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[Lecture Notes in Computer Science] Web and Big Data Volume 11268 (APWeb-WAIM 2018 International Workshops: MWDA, BAH, KGMA, DMMOOC, DS, Macau, China, July 23–25, 2018, Revised Selected Papers) || Spectroscopy-Based Food Internal Quality Evaluation with XGBoost Algorithm
摘要： In this paper, the combination of Near-Infrared (NIR) spectroscopy and a novel forecasting algorithm called XGBoost was proposed for food internal quality evaluation. First, the original NIR spectral data was preprocessed by Savitzky-Golay smoothing method to reduce the influence of noises. Secondly, the preprocessed spectra was submitted to PCA to extract essential information. Finally, the model was established by using the XGBoost algorithm. The performance of the proposed model was examined by comparing with different models including back propagation neural network (BPNN) and support vector regression (SVR). The results showed that the new proposed model outperformed other two models and this XGBoost-based tool was suitable for food internal quality control.
关键词： Internal quality forecasting,Food,NIR spectroscopy,XGBoost
更新于2025-09-09 09:28:46
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Quantification of Different Forms of Iron from Intact Soil Cores of Paddy Fields with Vis-NIR Spectroscopy
摘要： Iron (Fe) occurs in almost all soils and the analysis of various forms of Fe in the soil is of great pedological interest. Very little is known, however, about how visible and near-infrared (Vis-NIR) spectroscopy performs in intact soil cores of paddy fields for quantifying Fe concentrations. Our objective was to evaluate the feasibility of Vis-NIR spectroscopy of intact soil cores for rapid determination of the four Fe forms: total Fe (Fet), pyrophosphate-extractable Fe (Fep), dithionite-citrate-bicarbonate extractable Fe (Fed), and oxalate-extractable Fe (Feo). A total of 148 intact soil cores in Yujiang County, China, were sampled, and Vis-NIR spectra (350–2500 nm) were sectioned and scanned on four horizontal surfaces (5-cm depth intervals) of each soil core in the laboratory. Partial least squares regression (PLSR) and support vector machine regression (SVMR) models were compared using 70% of the section samples for calibration and 30% for independent validation. Results showed that the nonlinear SVMR models performed better than the PLSR models for the predictions of all Fe forms. The SVMR models produced the best predictions in the independent validation set for Fed (RMSEP = 2.223; R2 P = 0.85; RPDP = 2.59), Fet (RMSEP = 3.693; R2 P = 0.82; RPDP = 2.32), and Fep (RMSEP = 0.086; R2 P = 0.79; RPDP = 2.17). It was concluded that Vis-NIR spectroscopy coupled with SVMR is suitable for quantitatively determining different Fe forms in intact soil cores of paddy fields.
关键词： intact soil cores,iron forms,SVMR,Vis-NIR spectroscopy,paddy fields,PLSR
更新于2025-09-09 09:28:46
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Accurate Identification of the Sex and Species of Silkworm Pupae Using Near Infrared Spectroscopy
摘要： The present study proposes a novel method to discriminate the sex and species of silkworm pupae using NIR spectroscopy (800–2778 nm). The spectra from 840 silkworm pupae were collected then divided into a calibration set (700) and a test set (140) using the Kennard–Stone (KS) algorithm. The recognition models were built using the radial basis function and neural network (RBF–NN) and support vector machine (SVM) approaches. The species and sex identi?cation results using the RBF–NN and SVM models based on full spectral data achieved a low accuracy of 5% and 33.57%, respectively. To improve the accuracy and decrease the processing time, both principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the data dimensions. The performance of the optimized SVM model (92.14%) was much better than the RBF–NN model (19.29%) based on PCA. Overall, the best discrimination results were obtained using the RBF–NN and SVM models based on LDA, providing an accuracy of 100%. These promising results have shown that the LDA–SVM and LDA–RBF–NN models can accurately recognize the sex and species of silkworm pupae using NIR spectroscopy.
关键词： silkworm pupa,species,NIR spectroscopy,sex
更新于2025-09-09 09:28:46
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Detection of minced lamb and beef fraud using NIR spectroscopy
摘要： In this study, the feasibility of NIR spectroscopy to detect different types of meat fraud in both minced lamb and beef was investigated. For this, a multivariate chemometric approach was used to identify the most useful pre-processing techniques to discriminate between pure lamb and beef and adulterated samples. The results obtained in this study suggest that it is possible to use NIRS to distinguish pure from adulterated minced meat with acceptable precision and accuracy. Rates of classification between 78.95 and 100% were achieved for the validation sets. Higher % CC samples were obtained for samples mixed with pork, meat of Lidia breed cattle and foal meat than for samples adulterated with chicken, where the lowest rates of classification were achieved in both lamb and beef. Additionally, identification of adulteration of meat of Lidia breed cattle in minced beef at 2% was achieved. Furthermore, best classification results were obtained for minced beef mixed with foal meat with a 100% of samples correctly classified indicating that inclusion of foal meat in minced beef at 1% and higher can be detected by using NIRS. Regarding pre-processing techniques, in general, the most powerful ones to classify both groups of samples (pure and mixed) were those orientated to reduce the scatter, MSC and SNV, and, those to correct peak overlaps, 1st and 2nd Der.
关键词： adulteration detection,NIR spectroscopy,minced lamb,chemometrics,meat fraud,minced beef
更新于2025-09-09 09:28:46
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[IEEE 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) - Coimbatore, India (2018.3.1-2018.3.3)] 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) - Non Invasive Blood Glucose Detection along with an Assistive Diabetes Monitoring App
摘要： A Diabetes monitoring method has been developed using the blood glucose measurement made non-invasively. Glucose is the prime factor which is considered for the diagnosis of Diabetes mellitus. The common method for determining the glucose content in blood involves hand pricking by taking the blood samples. Regular pricking can cause pain and inconvenience which can even lead to calluses on the fingers and poor blood flow. Non-invasive technique is the recent advancement which does not involve drawing of any human interstitial fluids. The paper involves the design of a noninvasive glucose level detector using the Near Infrared (NIR) Spectroscopy concept and a smart device. The signals send through the fingertip will be absorbed based on vibrational movements. The result is obtained as voltage variations. The data thus obtained is then interfaced to an android device via Bluetooth to a monitoring android application which assist in keeping track of the glucose level.
关键词： Arduino,NIR Spectroscopy,Non invasive,Glucose,Diabetes,Android app
更新于2025-09-09 09:28:46
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Tracing the Geographical Origin of Lentils (Lens culinaris Medik.) by Infrared Spectroscopy and Chemometrics
摘要： The feasibility of applying the infrared spectroscopy for the geographical origin traceability of lentils from two different countries (Italy and Canada) was investigated. In particular, lentil samples were analyzed by Fourier transform near- and mid-infrared (FT-NIR and FT-MIR) spectroscopy and then discriminated by applying supervised models, i.e., linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA). To avoid LDA overfitting, two variable strategies were adopted, i.e., a variable reduction by principal component analysis and a variable compression by wavelet packet transform algorithm. FT-MIR models were more discriminating compared to FT-NIR ones with prediction abilities ranging from 98 to 100% and from 91 to 100% for cross- and external validation, respectively. The combination of the FT-MIR and FT-NIR data did not improve the model performances. These findings demonstrated the suitability of the FT-MIR spectroscopy, in combination with supervised pattern recognition techniques, to successfully classify lentils according to their geographical origin.
关键词： Lentils,FT-NIR spectroscopy,FT-MIR spectroscopy,Partial least squares discriminant analysis,Geographical origin,Linear discriminant analysis
更新于2025-09-04 15:30:14