Determination of Total Viable Count in Rainbow-Trout Fish Fillets Based on Hyperspectral Imaging System and Different Variable Selection and Extraction of Reference Data Methods

2018 ◽  
Vol 11 (12) ◽  
pp. 3481-3494 ◽  
Author(s):  
Sara Khoshnoudi-Nia ◽  
Marzieh Moosavi-Nasab ◽  
Seyed Mehdi Nassiri ◽  
Zohreh Azimifar
Keyword(s):  
Rainbow Trout ◽  
Variable Selection ◽  
Hyperspectral Imaging ◽  
Reference Data ◽  
Imaging System ◽  
Viable Count ◽  
Total Viable Count ◽  
Fish Fillets

scholarly journals Nondestructive and rapid determination of lignocellulose components of biofuel pellet using online hyperspectral imaging system

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Xuping Feng ◽  
Chenliang Yu ◽  
Xiaodan Liu ◽  
Yunfeng Chen ◽  
Hong Zhen ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Rapid Determination

scholarly journals Physical and microbial qualities of raw milk collected from Bangladesh Agricultural University dairy farm and the surrounding villages

1970 ◽  
Vol 6 (2) ◽  
pp. 217-221 ◽  
Author(s):  
MTG Khan ◽  
MA Zinnah ◽  
MP Siddique ◽  
MHA Rashid ◽  
MA Islam ◽  
...  
Keyword(s):  
Microbiological Quality ◽  
Dairy Farm ◽  
Raw Milk ◽  
Viable Count ◽  
Physical Parameters ◽  
Total Viable Count ◽  
Coliform Count ◽  
Milk Samples

The present study was undertaken with the aim of investigating the physical parameters (e.g. organoleptic and specific gravity of raw milk) and also to study the microbiological quality of raw milk (total viable count, Coliform count and Staphylococcal count) from different villages and Bangladesh Agricultural University (BAU) Dairy Farm of Mymensingh District of Bangladesh, during the period from July to November 2007. A total number of 100 raw milk samples were collected at morning and evening from BAU dairy farm and surrounding four villages of BAU campus. The organoleptic and bacteriological qualities of each sample were analyzed. The organoleptic examination included taste panel score to assess consumer's acceptance and the bacteriological analysis comprised enumeration of total viable count (TVC), total colifrom count (TCC) and total staphylococcal count (TSC) for the determination of sanitary quality. The organoleptic quality of the milk samples is more or less same except the Churkhai milk samples which had flat taste (in 16% milk sample). The average values of TVC/ml were log 5.920, 5.934, 6.007, 6.075 and 6.127 for BAU Dairy Farm, Boira, Shutiakhali, Churkahai and Paglabazar respectively; coliform count were log 2.501, 2.522, 2.550, 2.620 and 2.619 respectively; staphylococcal count were log 2.832, 2.812, 2.866, 2.931 and 2.988 respectively. So, it may be concluded that the raw milk samples of BAU Dairy Farm were superior to others collected from the selected villages which may be due to maintaining better hygienic condition. Key words: Raw milk, physical and microbial quality   doi: 10.3329/bjvm.v6i2.2339 Bangl. J. Vet. Med. (2008). 6 (2): 217-221

scholarly journals Application of hyperspectral imaging system to discriminate different diets of live Rainbow trout (Oncorhynchus mykiss)

2018 ◽  
Author(s):  
Mohammadmehdi Saberioon ◽  
Petr Cisar ◽  
Laurent Labbé ◽  
Pavel Souček ◽  
Pablo Pelissier
Keyword(s):  
Rainbow Trout ◽  
Oncorhynchus Mykiss ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Machine Learning Algorithms ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Classification Models ◽  
Fish Skin ◽  
Classification Rate

The main aim of this study was to evaluate the feasibility of hyperspectral imagery for determining the influence of different diets on fish skin. Rainbow trout (Oncorhynchus mykiss) were fed either a commercial based diet (N= 80) or a 100 % plant-based diet (N = 80). Hyperspectral images were made using a push-broom hyperspectral imaging system in the spectral region of 394-1009 nm. All images were calibrated using dark and white reference and the average spectral data from the region of interest were extracted. Six spectral pre-treatment methods were used, including Savitzky-Golay (SG), First Derivative(FD), Second Derivative (SD), Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) then a support vector machine (SVM) with linear kernel was applied to establish the classification models. Additionally, the Genetic algorithm (GA) was used to select optimal wavelengths to reduce the high dimensionality from hyperspectral images in order to decrease the computational costs and simplify the classification models. Overall classification models established from full wavelengths and selected wavelengths showed the good performance (Correct Classification Rate (CCR) = 0.871, Kappa = 0.741) when coupled with SG. The overall results indicate that the integration of Vis/NIR hyperspectral imaging system and machine learning algorithms has promise for discriminating different diets based on the live fish skin.

Determination of metmyoglobin in cooked tan mutton using Vis/NIR hyperspectral imaging system

2020 ◽  
Vol 85 (5) ◽  
pp. 1403-1410
Author(s):  
Ruirui Yuan ◽  
Guishan Liu ◽  
Jianguo He ◽  
Chao Ma ◽  
Lijuan Cheng ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System
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