scholarly journals Reflected Light Spectrometry and AI-Based Data Analysis for Detection of Rapid Chicken Eggshell Change Caused by Mycoplasma Synoviae

2021 ◽  
Vol 11 (17) ◽  
pp. 7799
Author(s):  
Anna Pakuła ◽  
Sławomir Paśko ◽  
Olimpia Kursa ◽  
Robert Komar

Mycoplasma synoviae (MS) is a pathogen that causes economic losses in the poultry industry. It can be transmitted, amongst others, via the respiratory tract and spread relatively quickly. As such, MS infections are mainly controlled by maintaining MS-free breeder flocks. Routine diagnosis for the detection of MS may be based on serological, culture, and molecular tests. Here, we propose an optical solution where AI-based analysis of spectral data obtained from the light reflected from the eggshells is used to determine whether they originate from healthy or Mycoplasma synoviae-infected hens. The wavelengths proposed for spectral MS detection are limited to those of VIS and NIR DPSS lasers, which are freely accessible on market. The results are satisfactory: for white eggshells, the F-score is over 95% for five different combinations of wavelengths (using eight or nine wavelengths); for brown eggshells, the F-score is above 85%, also for five different combinations of 6–9 wavelengths.

2011 ◽  
Vol 76 (9) ◽  
pp. 1133-1139 ◽  
Author(s):  
Pham Thi Nhat Trinh ◽  
Nguyen Cong Hao ◽  
Phan Thanh Thao ◽  
Le Tien Dung

From the ethanol extract of Drynaria fortunei (KUNZE) J. Sm., a new phenylpropanoid glycoside, fortunamide (1), was isolated and characterized by spectroscopic methods. Together with a new glycoside, 9 known compounds, including three curcuminoids (2–4), two isoprenylated flavonoids (5, 6), two flavonoids (7, 8), one monoterpenoid (9) and one phenolic acid (10) were isolated and identified by spectral data analysis from the rhizomes of Drynaria fortunei (KUNZE) J. Sm. Eight of them were isolated from Drynaria fortunei (KUNZE) J. Sm. for the first time.


Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 915
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190–400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky–Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250–400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.


Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 18
Author(s):  
Michael Li ◽  
Santoso Wibowo ◽  
Wei Li ◽  
Lily D. Li

Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis problem, which essentially is a class of inverse problems. It requires determining the structural parameters of a physical sample from the given spectroscopic curves. We proposed that the unknown target inverse function is approximated by an ELM through adding a linear neuron to correct the localized effect aroused by Gaussian basis functions. Unlike the conventional methods involving intensive numerical computations, under the new conceptual framework, the task of performing spectral data analysis becomes a learning task from data. As spectral data are typical high-dimensional data, the dimensionality reduction technique of principal component analysis (PCA) is applied to reduce the dimension of the dataset to ensure convergence. The proposed conceptual framework is illustrated using a set of simulated Rutherford backscattering spectra. The results have shown the proposed method can achieve prediction inaccuracies of less than 1%, which outperform the predictions from the multi-layer perceptron and numerical-based techniques. The presented method could be implemented as application software for real-time spectral data analysis by integrating it into a spectroscopic data collection system.


2009 ◽  
Vol 72 (16-18) ◽  
pp. 3590-3601 ◽  
Author(s):  
Frank-Michael Schleif ◽  
Thomas Villmann ◽  
Matthias Ongyerth

2012 ◽  
Vol 67 (11-12) ◽  
pp. 580-586 ◽  
Author(s):  
Mohammad Aslam ◽  
Mohammed Ali ◽  
Rameshwar Dayal ◽  
Kalim Javed

Phytochemical investigations of the methanolic extract of the fruits of Peucedanum grande C. B. Clarke (Apiaceae) led to the identification of three coumarins and a naphthyl labdanoate diarabinoside characterized as 5-hydroxy-6-isopranyl coumarin (1), 5,6-furanocoumarin (2), 7-methoxy-5,6-furanocoumarin (3), and labdanyl-3α-ol-18-(3’’’-methoxy-2’’’- naphthyl-oate)-3α-L-arabinofuranosyl-(2’→1’’)-α-L-arabinofuranoside (4). The structures of these compounds were identified on the basis of spectral data analysis and chemical reactions. The methanolic extract and 4 showed nephroprotective activity against gentamicininduced nephrotoxicity in Wistar rats.


2013 ◽  
Vol 43 (7) ◽  
pp. 1230-1237 ◽  
Author(s):  
Leandro do Carmo Rezende ◽  
Lucas Maciel Cunha ◽  
Cristina Mara Teixeira ◽  
Paulo Roberto de Oliveira ◽  
Nelson Rodrigo da Silva Martins

The poultry industry is characterized for its constant search for productivity and profitability, which are based on flock health status. Brazilian Commercial laying hens (Gallus gallus domesticus) have been impacted significantly by mite infestations. This review aims to compile the literature on the occurrence, economic losses, biology, epidemiology and control of mite species considered important for the Brazilian laying poultry industry. The national experience was compared with practices of other countries and a scarcity of studies on this subject in Brazil was evident. The poultry industry has prioritized the use of pesticides to control infestations with little regard for the adverse effects. In this context, the integrated control programs using several strategies simultaneously constitute the best alternative to mite control. Integrated control programs involve measures of chemical, physical and biological nature, as well as attention to cultural aspects. However, studies should be performed aiming at the development of new control methods, evaluating the adequacy of practices developed in other countries to the national reality.


2019 ◽  
Vol 3 (2) ◽  
Author(s):  
M. E. Von Staden ◽  
M. D. Byron ◽  
T. R. Jarvis ◽  
X. Zhang ◽  
C. A. Crist ◽  
...  

ObjectivesThe woody breast (WB) myopathy has caused economic losses in excess of $200 million annually to the poultry industry due to undesirable textural attributes and decreased functionality. This hardened muscle is also associated with other undesirable traits, such as white striping. This research was conducted to evaluate the impact of WB severity and genetic strain on consumer acceptability and sensory attributes of baked and fried broiler breast meat and elucidate the consumer acceptability of tumble-marinated, fajita meat made from broilers with normal (NOR), moderate (MOD) and severe (SEV) WB meat.Materials and MethodsFor descriptive analysis (n = 7 panelists, 10 panels) on baked and fried chicken, 3 × 5 factorial arrangements within randomized complete block designs with four replications were utilized to evaluate three severities of woody breast and the five different genetic strains that are most commonly used in the poultry industry. When significant differences (P < 0.05) occurred among treatments, Duncan’s multiple range test was utilized to separate treatment means. For consumer acceptability of baked chicken (n = 123 panelists), fried chicken (n = 125 panelists), and fajita meat (n = 127 panelists), randomized complete block designs with two replications were used to determine the impact of strain and severity on acceptability.ResultsFor baked chicken, SEV breasts were chewier, juicier, crunchier, and more cohesive (P < 0.05) than NOR and MOD breast samples. For fried chicken, SEV breasts were less tender and chewier (P < 0.05) than NOR breasts. In addition, SEV breasts were more cohesive and juicier, but less mushy (P < 0.05) than NOR and MOD breasts. For fried chicken samples, SEV breasts were crunchier (P < 0.05) than MOD breasts, which were crunchier (P < 0.05) than NOR breasts. The texture and overall acceptability of NOR baked breasts and fajita meat were preferred by consumers (P < 0.05) over SEV breasts. In contrast, the SEV breasts were preferred (P < 0.05) over the NOR breast meat for the fried chicken formulation. No differences existed (P > 0.05) in acceptability among genetic strains in baked or fried chicken breasts. The baked chicken consumer panelists were divided into 7 distinct clusters based on their sensory evaluation ratings. Cluster analysis indicated that 49% of panelists preferred NOR breast fillets, 21% preferred SEV, and 30% had no preference between NOR and WB (MOD, SEV) samples. The fried chicken consumer panelists were divided into 5 clusters, of which 65% preferred WB (MOD, SEV) over NOR, 29% preferred strain B over strain A, and 11% preferred strain A over strain B. The fajita chicken meat consumer panelists were divided into 5 clusters, of which 75% of panelists liked NOR breast samples, 72% liked MOD samples, and 45% liked SEV samples.ConclusionResults indicated that WB severity had a greater impact on sensory attributes and consumer acceptability than genetic strain. Higher WB severity created an undesirable texture that negatively impacted the acceptability of baked meat. However, the increased crunchiness and cohesiveness due to woodiness had a positive impact on the fried chicken acceptability. Results indicated that a large percentage of consumers rated baked, fried, and fajita samples as acceptable regardless of whether NOR or WB (MOD, SEV) meat was used, but some consumers did not like baked or fajita meat that was made from SEV WB meat.


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