Erratum to “Robust prediction models for quality parameters in Japanese plums (Prunus salicina L.) using NIR spectroscopy” [Postharvest Biol. Technol. 58 (2010) 176–184]

2011 ◽  
Vol 62 (1) ◽  
pp. 93-95
Author(s):  
Esmé D. Louw ◽  
Karen I. Theron
Keyword(s):  
Prediction Models ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Prunus Salicina ◽  
Robust Prediction

scholarly journals Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2195
Author(s):  
Lucas de Paula Corrêdo ◽  
Leonardo Felipe Maldaner ◽  
Helizani Couto Bazame ◽  
José Paulo Molin
Keyword(s):  
Sample Preparation ◽  
Prediction Models ◽  
Spectral Response ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Partial Least Square ◽  
Least Square ◽  
Sample Type ◽  
Sampling Strategies ◽  
Significant Difference

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

scholarly journals Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Sylvio Barbon ◽  
Ana Paula Ayub da Costa Barbon ◽  
Rafael Gomes Mantovani ◽  
Douglas Fernandes Barbin
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Chicken Meat ◽  
Poultry Meat ◽  
Spectral Information ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Meat Samples

Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIEL∗a∗b∗, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.

Evaluation of the effect of essential oil addition on the quality parameters and predicted shelf life of potato yogurt

2021 ◽  
Author(s):  
Zuyan He ◽  
Jia zheng ◽  
Laping He ◽  
Cuiqin Li ◽  
Penggang Hu ◽  
...  
Keyword(s):  
Essential Oil ◽  
Prediction Model ◽  
Shelf Life ◽  
Prediction Models ◽  
Antimicrobial Effect ◽  
Quality Parameters ◽  
Sensory Characteristics ◽  
Main Compound ◽  
Flavor Components ◽  
Volatile Flavor

Potato, the third most important food crop worldwide, is rich in nutrients but low in protein. By contrast, milk is rich in protein. Yogurts produced through the cofermentation of potatoes and milk is a highly nutritious food. In addition, the quality and shelf life of yogurts are hot topics in the dairy industry. The objective of this study was to explore the effect of the addition of essential oil (EO) on the shelf life and quality of potato yogurt. The antimicrobial effects of several EOs were compared, the effect of perilla leaf EO (PLEO) content on potato yogurt was discussed, and the volatile flavor components of PLEO and PLEO potato yogurt were detected. Furthermore, the effects of storage time and temperature on the pH, microbial counts, and sensory characteristics of PLEO potato yogurt were analyzed to establish a shelf life model. Results showed that PLEO had a good antimicrobial effect and was the appropriate EO. A total of 69 compounds were detected in PLEO, with limonene being the main compound. PLEO had an effect on the pH, sensory characteristics, and viable counts of potato yogurt during storage. The best PLEO addition amount was 0.04%. PLEO had a considerable influence on volatile flavor components, and the consumer acceptance of 0.04% PLEO potato yogurt was better than that of potato yogurt without PLEO in the later stage of storage. Moreover, the shelf life of potato yogurt with PLEO was 6 days longer than that of the control yogurt. PLEO also improved the content of active terpene substances in potato yogurt. The prediction models based on pH and sensory scores at 5 °C were established as A = A 0 e 0.00323 t  and A = A 0 e 0.00355 t , respectively. Comparing the accuracy factor and deviation factor of the models revealed that the sensory prediction model was more rational than the pH prediction model. Data from this study provided support to the potential industrial application and shelf life prediction of EO yogurt.

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

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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