NIR Spectroscopy for Discriminating and Predicting the Sensory Profile of Dry-Cured Beef “Cecina”

For Protected Geographical Indication (PGI)-labeled products, such as the dry-cured beef meat “cecina de León”, a sensory analysis is compulsory. However, this is a complex and time-consuming process. This study explores the viability of using near infrared spectroscopy (NIRS) together with artificial neural networks (ANN) for predicting sensory attributes. Spectra of 50 samples of cecina were recorded and 451 reflectance data were obtained. A feedforward multilayer perceptron ANN with 451 neurons in the input layer, a number of neurons varying between 1 and 30 in the hidden layer, and a single neuron in the output layer were optimized for each sensory parameter. The regression coefficient R squared (RSQ > 0.8 except for odor intensity) and mean squared error of prediction (MSEP) values obtained when comparing predicted and reference values showed that it is possible to predict accurately 23 out of 24 sensory parameters. Although only 3 sensory parameters showed significant differences between PGI and non-PGI samples, the optimized ANN architecture applied to NIR spectra achieved the correct classification of the 100% of the samples while the residual mean squares method (RMS-X) allowed 100% of non-PGI samples to be distinguished.

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Optimizing Rice Near-Infrared Models Using Fractional Order Savitzky–Golay Derivation (FOSGD) Combined with Competitive Adaptive Reweighted Sampling (CARS)

Applied Spectroscopy ◽

10.1177/0003702819895799 ◽

2020 ◽

Vol 74 (4) ◽

pp. 417-426 ◽

Cited By ~ 2

Author(s):

Zhenzhen Xia ◽

Jie Yang ◽

Jing Wang ◽

Shengpeng Wang ◽

Yan Liu

Keyword(s):

Fractional Order ◽

Near Infrared ◽

Mean Squared Error ◽

Nir Spectroscopy ◽

Predictive Ability ◽

Selection Method ◽

Wavelength Selection ◽

Root Mean Squared Error ◽

Squared Error ◽

Accuracy And Stability

Developing a rapid and stable method for analyzing the quality parameters of rice is important. Near-infrared (NIR) spectroscopy combined with chemometric techniques have been used to predict the critical contents of rice and shown its accuracy and stability. To further improve the predictive ability, we combine the derivative method of fractional order Savitzky–Golay derivation (FOSGD) with the wavelength selection method of competitive adaptive reweighted sampling (CARS). Compared with the traditional integer order Savitzky–Golay derivation (IOSGD), the FOSGD could improve the resolution ratio of the raw spectra more effectively. The wavelength selection method, CARS, could further extract the informative variables from the processed spectra. Four key contents of rice samples, including moisture, amylose, chalkiness degree, and gel consistency, were utilized to validate this method. The prediction results indicated that partial least squares (PLS) models optimized with FOSGD-CARS own higher accuracy and stability with smaller the root mean squared error of cross validations (RMSECVs) and root mean squared error of predictions (RMSEPs). The proposed method is convenient and provides a practical alternative for rice analysis.

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Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽

10.3390/pr9020196 ◽

2021 ◽

Vol 9 (2) ◽

pp. 196

Author(s):

Araz Soltani Nazarloo ◽

Vali Rasooli Sharabiani ◽

Yousef Abbaspour Gilandeh ◽

Ebrahim Taghinezhad ◽

Mariusz Szymanek ◽

...

Keyword(s):

Near Infrared ◽

Cross Validation ◽

Low Cost ◽

Moving Average ◽

Nir Spectroscopy ◽

Control Treatment ◽

Residue Detection ◽

Non Destructive ◽

Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

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Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

10.5194/egusphere-egu21-3896 ◽

2021 ◽

Author(s):

Iva Hrelja ◽

Ivana Šestak ◽

Igor Bogunović

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Spectral Data ◽

Near Infrared ◽

Mean Squared Error ◽

Nir Spectroscopy ◽

Soil Samples ◽

Least Squares Regression ◽

Macro Scale ◽

Sentinel 2

Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.The study is performed in Mediterranean Croatia (44&#176; 05&#8217; N; 15&#176; 22&#8217; E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed Quercus ssp. and Juniperus ssp. forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22nd 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31st and September 5th 2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSEp) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSEp (Spectroscopy dataset RMSEp = 1.91; Sentinel-2 dataset RMSEp = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.Keywords: remote sensing, soil spectroscopy, wildfires, soil organic matterAcknowledgment: This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Per&#269;in is acknowledged for her cooperation during the laboratory work.

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Measuring The Tensile Strain of Wood By Visible And Near-Infrared Spatially Resolved Spectroscopy

10.21203/rs.3.rs-570550/v1 ◽

2021 ◽

Author(s):

Ma Te ◽

Tetsuya Inagaki ◽

Masato Yoshida ◽

Mayumi Ichino ◽

Satoru Tsuchikawa

Keyword(s):

Light Scattering ◽

Prediction Accuracy ◽

Tensile Strain ◽

Near Infrared ◽

Mean Squared Error ◽

Calibration Model ◽

Spatially Resolved ◽

Squared Error ◽

Stiffness Evaluation ◽

Spatially Resolved Spectroscopy

Abstract Wood has various mechanical properties, so stiffness evaluation is critical for quality management. Using conventional strain gauges constantly is high cost, also challenging to measure precious wood materials due to the use of strong adhesive. This study demonstrates the correlation between light scattering changes inside the wood cell walls and tensile strain. A multifiber-based visible-near-infrared (Vis–NIR) spatially resolved spectroscopy (SRS) system was designed to rapidly and conventiently acquire such light scattering changes. For the preliminary experiment, samples with different thicknesses were measured to evaluate the influence of thickness. The differences in Vis–NIR SRS spectral data diminish with an increase in sample thickness, which suggests that the SRS method can successfully measure the whole strain (i.e., surface and inside) of wood samples. Then, for the primary experiment, 18 wood samples with the same thickness (2 mm) were tested to construct a strain calibration model. The prediction accuracy was characterized by a determination coefficient (R2) of 0.86 with a root mean squared error (RMSE) of 297.89 με for five-fold cross-validation; for test validation, The prediction accuracy was characterized by an R2 of 0.82 and an RMSE of 345.44 με.

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Prediction of Sensory Parameters of Cured Ham: A Study of the Viability of the Use of NIR Spectroscopy and Artificial Neural Networks

Sensors ◽

10.3390/s20195624 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5624

Author(s):

Pedro Hernández-Ramos ◽

Ana María Vivar-Quintana ◽

Isabel Revilla ◽

María Inmaculada González-Martín ◽

Miriam Hernández-Jiménez ◽

...

Keyword(s):

Neural Network ◽

Nir Spectroscopy ◽

Sensory Profile ◽

Training Algorithm ◽

Ann Model ◽

Fiber Optic Probe ◽

Organoleptic Characteristics ◽

Dry Cured Ham ◽

Artificial Neural ◽

Sensory Parameters

Dry-cured ham is a high-quality product owing to its organoleptic characteristics. Sensory analysis is an essential part of assessing its quality. However, sensory assessment is a laborious process which implies the availability of a trained tasting panel. The aim of this study was the prediction of dry-ham sensory characteristics by means of an instrumental technique. To do so, an artificial neural network (ANN) model for the prediction of sensory parameters of dry-cured hams based on NIR spectral information was developed and optimized. The NIR spectra were obtained with a fiber-optic probe applied directly to the ham sample. In order to achieve this objective, the neural network was designed using 28 sensory parameters analyzed by a trained panel for sensory profile analysis as output data. A total of 91 samples of dry-cured ham matured for 24 months were analyzed. The hams corresponded to two different breeds (Iberian and Iberian x Duroc) and two different feeding systems (feeding outdoors with acorns or feeding with concentrates). The training algorithm and ANN architecture (the number of neurons in the hidden layer) used for the training were optimized. The parameters of ANN architecture analyzed have been shown to have an effect on the prediction capacity of the network. The Levenberg–Marquardt training algorithm has been shown to be the most suitable for the application of an ANN to sensory parameters

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Classification of Chinese Soybean Paste by Fourier Transform Near-Infrared (FT-NIR) Spectroscopy and Different Supervised Pattern Recognition

Food Analytical Methods ◽

10.1007/s12161-011-9331-0 ◽

2011 ◽

Vol 5 (4) ◽

pp. 928-934 ◽

Cited By ~ 15

Author(s):

Hui Jiang ◽

Guohai Liu ◽

Xiahong Xiao ◽

Shuang Yu ◽

Congli Mei ◽

...

Keyword(s):

Pattern Recognition ◽

Fourier Transform ◽

Near Infrared ◽

Nir Spectroscopy ◽

Soybean Paste

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Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.460-461.667 ◽

2011 ◽

Vol 460-461 ◽

pp. 667-672

Author(s):

Yun Zhao ◽

Xing Xu ◽

Yong He

Keyword(s):

Near Infrared ◽

Prediction Models ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Support Vector ◽

Training Set ◽

Test Set ◽

Svm Model

The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

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Data Mining for Healthcare Data: A Comparison of Neural Networks Algorithms

CogITo Smart Journal ◽

10.31154/cogito.v3i1.40.10-19 ◽

2017 ◽

Vol 3 (1) ◽

pp. 10

Author(s):

Debby E. Sondakh

Keyword(s):

Neural Networks ◽

Multilayer Perceptron ◽

Mean Squared Error ◽

Absolute Error ◽

Error Rates ◽

Large Dataset ◽

Training Time ◽

Healthcare Data ◽

Squared Error ◽

Hidden Layer

Classification has been considered as an important tool utilized for the extraction of useful information from healthcare dataset. It may be applied for recognition of disease over symptoms. This paper aims to compare and evaluate different approaches of neural networks classification algorithms for healthcare datasets. The algorithms considered here are Multilayer Perceptron, Radial Basis Function, and Voted Perceptron which are tested based on resulted classifiers accuracy, precision, mean absolute error and root mean squared error rates, and classifier training time. All the algorithms are applied for five multivariate healthcare datasets, Echocardiogram, SPECT Heart, Chronic Kidney Disease, Mammographic Mass, and EEG Eye State datasets. Among the three algorithms, this study concludes the best algorithm for the chosen datasets is Multilayer Perceptron. It achieves the highest for all performance parameters tested. It can produce high accuracy classifier model with low error rate, but suffer in training time especially of large dataset. Voted Perceptron performance is the lowest in all parameters tested. For further research, an investigation may be conducted to analyze whether the number of hidden layer in Multilayer Perceptron’s architecture has a significant impact on the training time.

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Visible-near infrared spectroscopy for detection of blood in sheep faeces

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033520927519 ◽

2020 ◽

Vol 28 (5-6) ◽

pp. 255-266 ◽

Cited By ~ 2

Author(s):

Elise A Kho ◽

Jill N Fernandes ◽

Andrew C Kotze ◽

Glen P Fox ◽

Maggy Lord ◽

...

Keyword(s):

Support Vector Machine ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Mean Squared Error ◽

Partial Least Square ◽

Least Square ◽

Support Vector ◽

Root Mean Squared Error ◽

Squared Error

Heavy infestations of the blood-sucking gastrointestinal nematodes, Haemonchus contortus can cause severe anaemia in sheep and leakage of blood into the faeces, leading to morbidity and mortality. Early and accurate diagnosis of infections is critical for timely treatment of sheep, minimizing production and sheep welfare impacts. In pursuit of a quick and easy measure of H. contortus infections, we investigated the use of portable visible near infrared spectrometers for detecting the presence of haemoglobin in sheep faeces as an indicator of H. contortus infection. Calibration models built within the 400–600 nm region by partial least square regression resulted in acceptable prediction accuracies (r 2 p > 0.70 and root mean squared error of prediction <2.64 µg Hb mg−1 faeces) for haemoglobin quantification using two spectrometers. The prediction results from support vector machine regression further improved the prediction of haemoglobin in moist sheep faeces (r 2 p > 0.87 and root mean squared error of prediction <2.00 µg haemoglobin mg−1 faeces). Based on a threshold for anthelmintic treatment of 3 µg Hb mg−1 faeces, both the partial least square and support vector machine models showed high sensitivity (89%) and high specificity (>77%). The specificity of the prediction model for detecting haemoglobin in sheep faeces may be improved by adding more variations in faecal composition into the calibration model. Our success in detecting haemoglobin in sheep faeces, following minimal sample preparation, suggests that with further development, vis–near infrared spectroscopy can provide a sensitive and convenient method for on-farm diagnosis of H. contortus infections.

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Near-Infrared Spectroscopy as a classification tool for agraz (Vaccinium meridionale Swartz)

DYNA ◽

10.15446/dyna.v87n213.82284 ◽

2020 ◽

Vol 87 (213) ◽

pp. 17-21

Author(s):

Nathalia María Forero-Cabrera ◽

Carolina Maria Sánchez-Sáenz

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Nir Spectroscopy ◽

Agricultural Products ◽

Maturity Stage ◽

New Techniques ◽

Classification Tool ◽

Suitable Technique

The importance of the selection and classification processes in the industry of agricultural products and the increase in the production of fruits make necessary the development and implementation of new techniques to efficiently perform these tasks. Techniques such as NIR spectroscopy have proved to have potential to accomplish this purpose. The aim of this research was to evaluate the performance of near infrared spectroscopy as a classification tool for agraz (Vaccinium meridionale Swartz), according to its state of maturity. In order to obtainthe classification models, the PCA and SIMCA methods were used. Results were obtained close to 100% accuracy in the classification for maturity stages 4 and 5 and between 81 and 90% for maturity stage 3. The NIR spectroscopy appears as a suitable technique for the classification of fruits of agraz according to their state of maturity.

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