scholarly journals Identification of Cold Spots Using Non-Destructive Hyperspectral Imaging Technology in Model Food Processed by Coaxially Induced Microwave Pasteurization and Sterilization

Foods ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 837 ◽  
Author(s):  
Aswathi Soni ◽  
Mahmoud Al-Sarayreh ◽  
Marlon M. Reis ◽  
Jeremy Smith ◽  
Kris Tong ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Control Sample ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Cold Spot ◽  
Maillard Reaction Products ◽  
Reaction Products ◽  
Analysis Model ◽  
Model Food ◽  
Microwave Pasteurization

The model food in this study known as mashed potato consisted of ribose (1.0%) and lysine (0.5%) to induce browning via Maillard reaction products. Mashed potato was processed by Coaxially Induced Microwave Pasteurization and Sterilization (CiMPAS) regime to generate an F0 of 6–8 min and analysis of the post-processed food was done in two ways, which included by measuring the color changes and using hyperspectral data acquisition. For visualizing the spectra of each tray in comparison with the control sample (raw mashed-potato), the mean spectrum (i.e., mean of region of interest) of each tray, as well as the control sample, was extracted and then fed to the fitted principal component analysis model and the results coincided with those post hoc analysis of the average reflectance values. Despite the presence of a visual difference in browning, the Lightness (L) values were not significantly (p < 0.05) different to detect a cold spot among a range of 12 processed samples. At the same time, hyperspectral imaging could identify the colder trays among the 12 samples from one batch of microwave sterilization.

scholarly journals Characterization of Pharmaceutical Tablets Using UV Hyperspectral Imaging as a Rapid Line to Line Analysis Tool

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4436
Author(s):  
Mohammad Al Ktash ◽  
Mona Stefanakis ◽  
Barbara Boldrini ◽  
Edwin Ostertag ◽  
Marc Brecht
Keyword(s):  
Hyperspectral Imaging ◽  
Large Scale ◽  
Solid Phase ◽  
Ultra Violet ◽  
Principal Component ◽  
Ccd Camera ◽  
Conveyor Belt ◽  
Hyperspectral Data ◽  
Analysis Tool ◽  
Sample Set

A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were used for a comparison purpose. Sample set one comprises tablets of 100% API and sample set two consists of commercially available painkiller tablets. Reference measurements were performed on the pure APIs in liquid solutions (transmission) and in solid phase (reflection) using a commercial UV spectrometer. The spectroscopic part of the prototype is based on a pushbroom imager that contains a spectrograph and charge-coupled device (CCD) camera. The tablets were scanned on a conveyor belt that is positioned inside a tunnel made of polytetrafluoroethylene (PTFE) in order to increase the homogeneity of illumination at the sample position. Principal component analysis (PCA) was used to differentiate the hyperspectral data of the drug samples. The first two PCs are sufficient to completely separate all samples. The rugged design of the prototype opens new possibilities for further development of this technique towards real large-scale application.

Analysis of the non-volatile Maillard reaction products formed in an extrusion-cooked model food system

1998 ◽  
Vol 61 (4) ◽  
pp. 521-524 ◽  
Author(s):  
Jennifer M Ames ◽  
Aklile B Defaye ◽  
Richard G Bailey ◽  
Lisa Bates
Keyword(s):  
Maillard Reaction ◽  
Food System ◽  
Maillard Reaction Products ◽  
Reaction Products ◽  
Model Food

scholarly journals Drought Stress Detection in Juvenile Oilseed Rape Using Hyperspectral Imaging with a Focus on Spectra Variability

2020 ◽  
Vol 12 (20) ◽  
pp. 3462
Author(s):  
Wiktor R. Żelazny ◽  
Jan Lukáš
Keyword(s):  
Drought Stress ◽  
Hyperspectral Imaging ◽  
Oilseed Rape ◽  
Treatment Effects ◽  
Vegetation Index ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Stress Detection ◽  
Mean Values ◽  
Standard Deviations

Hyperspectral imaging (HSI) has been gaining recognition as a promising proximal and remote sensing technique for crop drought stress detection. A modelling approach accounting for the treatment effects on the stress indicators’ standard deviations was applied to proximal images of oilseed rape—a crop subjected to various HSI studies, with the exception of drought. The aim of the present study was to determine the spectral responses of two cultivars, ‘Cadeli’ and ‘Viking’, representing distinctive water management strategies, to three types of watering regimes. Hyperspectral data cubes were acquired at the leaf level using a 2D frame camera. The influence of the experimental factors on the extent of leaf discolorations, vegetation index values, and principal component scores was investigated using Bayesian linear models. Clear treatment effects were obtained primarily for the vegetation indexes with respect to the watering regimes. The mean values of RGI, MTCI, RNDVI, and GI responded to the difference between the well-watered and water-deprived plants. The RGI index excelled among them in terms of effect strengths, which amounted to −0.96[−2.21,0.21] and −0.71[−1.97,0.49] units for each cultivar. A consistent increase in the multiple index standard deviations, especially RGI, PSRI, TCARI, and TCARI/OSAVI, was associated with worsening of the hydric regime. These increases were captured not only for the dry treatment but also for the plants subjected to regeneration after a drought episode, particularly by PSRI (a multiplicative effect of 0.33[0.16,0.68] for ‘Cadeli’). This result suggests a higher sensitivity of the vegetation index variability measures relative to the means in the context of the oilseed rape drought stress diagnosis and justifies the application of HSI to capture these effects. RGI is an index deserving additional scrutiny in future studies, as both its mean and standard deviation were affected by the watering regimes.

Detection of Nitrogen Content Changes of Rape Leaf Using Hyperspectral Imaging

2011 ◽  
Vol 204-210 ◽  
pp. 131-134 ◽  
Author(s):  
Wei Zou ◽  
Hui Fang ◽  
Yi Dan Bao ◽  
Yong He
Keyword(s):  
Neural Network ◽  
Nitrogen Content ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Spad Value ◽  
Good Classification ◽  
Optimal Wavelength ◽  
Artificial Neural Network Ann

Hyperspectral imaging (400-1000nm) and artificial neural network (ANN) techniques were investigated for the detection of nitrogen content changes of rape leaf. Measuring SPAD value of rape leaf by using SPAD (Soil and Plant Analyzer Development).A hyperspectral imaging system was established to acquire hyperspectral data. Principal component analysis(PCA) was used to obtain principal component images, as well as to select the optimal wavelength(s). ANN was applied to establish the model between the spectral reflection values and SPAD values. The prediction results were obtained for the nitrogen content of rape leaf with the correlation of prediction of R=0.9237. The results show that the hyperspectral imaging has good classification on different nitrogen content of rape leaf.

scholarly journals Comparative Study on Phenolic Content and Antioxidant Activity of Different Malt Types

Antioxidants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1124
Author(s):  
Vesela Shopska ◽  
Rositsa Denkova-Kostova ◽  
Mina Dzhivoderova-Zarcheva ◽  
Desislava Teneva ◽  
Petko Denev ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Antioxidant Activity ◽  
Maillard Reaction ◽  
Phenolic Content ◽  
Principal Component ◽  
Component Analysis ◽  
Raw Material ◽  
Antioxidant Potential ◽  
Maillard Reaction Products ◽  
Reaction Products

Malt is the main raw material for beer production, which determines not only its taste and aroma profile, but to a large extent its biological value, as well. The aim of the present research was to determine the antioxidant profile of different malt types as a basis for the development of new types of beer with increased antioxidant activity. In the present study the main brewing characteristics, the phenolic profile and the antioxidant potential of 20 malt types used in craft breweries in Bulgaria have been examined. The main brewing characteristics have been determined by the standardized methods of the European Brewing Convention. Malt phenolic content was determined by two methods, and antioxidant potential by five different methods. Based on a statistical factor analysis performed by the principal component analysis, it was confirmed that there was a relationship between malt color and phenolic compounds content. The principal component analysis confirmed that there was a link between the content of the Maillard reaction products and malt biological activity. Malts with the highest degree of heat treatment were characterized by the highest antioxidant activity, which was due to the content of Maillard reaction products with antioxidant capacity.

Thermal pasteurization process evaluation using mashed potato model food with Maillard reaction products

LWT ◽  
2017 ◽  
Vol 82 ◽  
pp. 454-463 ◽  
Author(s):  
Ellen R. Bornhorst ◽  
Juming Tang ◽  
Shyam S. Sablani ◽  
Gustavo V. Barbosa-Cánovas
Keyword(s):  
Process Evaluation ◽  
Maillard Reaction ◽  
Maillard Reaction Products ◽  
Reaction Products ◽  
Model Food ◽  
Thermal Pasteurization

scholarly journals Detection of Adulterated Honey by Fluorescence Excitation-Emission Matrices

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Tatjana Dramićanin ◽  
Lea Lenhardt Acković ◽  
Ivana Zeković ◽  
Miroslav D. Dramićanin
Keyword(s):  
Sample Preparation ◽  
Fluorescence Spectroscopy ◽  
Principal Component ◽  
Maillard Reaction Products ◽  
Reaction Products ◽  
Fluorescence Excitation ◽  
Linear Discriminant ◽  
Excitation Emission Matrices ◽  
Fluorescence Excitation Emission Matrices

Honey is a frequent target of adulteration through inappropriate production practices and origin mislabelling. Current methods for the detection of adulterated honey are time and labor consuming, require highly skilled personnel, and lengthy sample preparation. Fluorescence spectroscopy overcomes such drawbacks, as it is fast and noncontact and requires minimal sample preparation. In this paper, the application of fluorescence spectroscopy coupled with statistical tools for the detection of adulterated honey is demonstrated. For this purpose, fluorescence excitation-emission matrices were measured for 99 samples of different types of natural honey and 15 adulterated honey samples (in 3 technical replicas for each sample). Statistical t-test showed that significant differences between fluorescence of natural and adulterated honey samples exist in 5 spectral regions: (1) excitation: 240–265 nm, emission: 370–495 nm; (2) excitation: 280–320 nm, emission: 390–470 nm; (3) excitation: 260–285 nm, emission: 320–370 nm; (4) excitation: 310–360 nm, emission: 370–470 nm; and (5) excitation: 375–435 nm, emission: 440–520 nm, in which majority of fluorescence comes from the aromatic amino acids, phenolic compounds, and fluorescent Maillard reaction products. Principal component analysis confirmed these findings and showed that 90% of variance in fluorescence is accumulated in the first two principal components, which can be used for the discrimination of fake honey samples. The classification of honey from fluorescence data is demonstrated with a linear discriminant analysis (LDA). When subjected to LDA, total fluorescence intensities of selected spectral regions provided classification of honey (natural or adulterated) with 100% accuracy. In addition, it is demonstrated that intensities of honey emissions in each of these spectral regions may serve as criteria for the discrimination between natural and fake honey.

scholarly journals Development of model food systems for thermal pasteurization applications based on Maillard reaction products

LWT ◽  
2017 ◽  
Vol 75 ◽  
pp. 417-424 ◽  
Author(s):  
Ellen R. Bornhorst ◽  
Juming Tang ◽  
Shyam S. Sablani ◽  
Gustavo V. Barbosa-Cánovas
Keyword(s):  
Maillard Reaction ◽  
Food Systems ◽  
Maillard Reaction Products ◽  
Reaction Products ◽  
Model Food ◽  
Thermal Pasteurization

An Input–Process–Output Model of Pilot Core Competencies

2017 ◽  
Vol 7 (2) ◽  
pp. 78-85 ◽  
Author(s):  
Heikki Mansikka ◽  
Don Harris ◽  
Kai Virtanen
Keyword(s):  
Path Analysis ◽  
Team Performance ◽  
Core Competencies ◽  
Core Competency ◽  
Principal Component ◽  
Analysis Model ◽  
Input Process ◽  
The Core ◽  
Performance Framework ◽  
Process Output

Abstract. The aim of this study was to investigate the relationship between the flight-related core competencies for professional airline pilots and to structuralize them as components in a team performance framework. To achieve this, the core competency scores from a total of 2,560 OPC (Operator Proficiency Check) missions were analyzed. A principal component analysis (PCA) of pilots’ performance scores across the different competencies was conducted. Four principal components were extracted and a path analysis model was constructed on the basis of these factors. The path analysis utilizing the core competencies extracted adopted an input–process–output’ (IPO) model of team performance related directly to the activities on the flight deck. The results of the PCA and the path analysis strongly supported the proposed IPO model.

Sign in / Sign up

Export Citation Format

Share Document