scholarly journals Fermentation Quality and Aroma Profile of Winter Cereals and Italian Ryegrass Plus Winter Cereal Mixture Silages

2021 ◽  
Author(s):  
Alemayehu Worku Babu ◽  
Tamás Tóth ◽  
Szilvia Orosz ◽  
Hedvig Fébel ◽  
László Kacsala ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Principal Component ◽  
Italian Ryegrass ◽  
Ethyl Esters ◽  
Winter Cereal ◽  
Aroma Profile ◽  
Linear Discriminant ◽  
Winter Cereals ◽  
Fermentation Quality ◽  
Mold And Yeast

Abstract During silage making microbial fermentation produces an array of end products which can influence the odour of the final silage and can also change many nutritive aspects of a forage. The objective of this study was to evaluate the fermentation quality and aroma profile of winter cereals and Italian ryegrass (Lolium multiflorum Lam., IRG) plus winter cereal mixture silages detected with an electronic nose. Four mixtures (mixture A: triticale, oats, barley and wheat; mixture B: triticale, barley and wheat; mixture C: IRG and oats; mixture D: IRG, oats, triticale, barley and wheat) were harvested, wilted and ensiled in laboratory-scale silos (n = 80) without additives. Mixture C had higher (P < 0.05) mold and yeast (Log10 CFU (colony forming unit)/g) counts compared to mixture B. Mixture B and C had higher acetic acid (AA) content than mixture A and D. The lactic acid (LA) content was higher for mixture B than mixture C. At the end of 90 days fermentation winter cereal mixture silages (mixture A and B) had similar aroma pattern, and mixture C was also similar to winter cereal silages. However, mixture D had different aromatic pattern than other ensiled mixtures. Both the principal component analysis (PCA) score plot for aroma profile and linear discriminant analysis (LDA) classification revealed that mixture D had different aroma profile than other mixture silages. The difference was caused by the presence of high ethanol and LA in mixture D. Ethyl esters such as ethyl 3-methyl pentanoate, 2-methylpropanal, ethyl acetate, isoamyl acetate and ethyl-3-methylthiopropanoate were found at different retention indices in mixture D silage. The low LA and higher mold and yeast count in mixture C silage caused off odour due to the presence of 3-methylbutanoic acid, a simple alcohol with unpleasant camphor-like odor. In general, the electronic nose (EN) results revealed that the ensiled mixtures were dominated by ethyl ester likely producing pleasant fruity odors which could increase the intake of ensiled mixtures. However, the technology is suitable in finding off odor compounds of ensiled forages that may likely reduce feed intake.

scholarly journals Aroma Profile, Microbial and Chemical Quality of Ensiled Green Forages Mixtures of Winter Cereals and Italian Ryegrass

Agriculture ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 512
Author(s):  
Alemayehu Worku ◽  
Tamás Tóth ◽  
Szilvia Orosz ◽  
Hedvig Fébel ◽  
László Kacsala ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Winter Barley ◽  
Italian Ryegrass ◽  
Chemical Quality ◽  
Winter Cereal ◽  
Aroma Profile ◽  
Winter Triticale ◽  
Winter Cereals ◽  
Mold And Yeast

The objective of this study was to evaluate the aroma profile, microbial and chemical quality of winter cereals (triticale, oats, barley and wheat) and Italian ryegrass (Lolium multiflorum Lam., IRG) plus winter cereal mixture silages detected with an electronic nose. Four commercial mixtures (mixture A (40% of two cultivars of winter triticale + 30% of two cultivars of winter oats + 20% of winter barley + 10% of winter wheat), mixture B (50% of two cultivars of winter triticale + 40% of winter barley + 10% of winter wheat), mixture C (55% of three types of Italian ryegrass + 45% of two cultivars of winter oat), mixture D (40% of three types of Italian ryegrass + 30% of two cultivars of winter oat + 15% of two cultivars of winter triticale + 10% of winter barley + 5% of winter wheat)) were harvested, wilted and ensiled in laboratory-scale silos (n = 80) without additives. Both the principal component analysis (PCA) score plot for aroma profile and linear discriminant analysis (LDA) classification revealed that mixture D had different aroma profile than other mixture silages. The difference was caused by the presence of high ethanol and LA in mixture D. Ethyl esters such as ethyl 3-methyl pentanoate, 2-methylpropanal, ethyl acetate, isoamyl acetate and ethyl-3-methylthiopropanoate were found at different retention indices in mixture D silage. The low LA and higher mold and yeast count in mixture C silage caused off odour due to the presence of 3-methylbutanoic acid, a simple alcohol with unpleasant camphor-like odor. At the end of 90 days fermentation winter cereal mixture silages (mixture A and B) had similar aroma pattern, and mixture C was also similar to winter cereal silages. However, mixture D had different aromatic pattern than other ensiled mixtures. Mixture C had higher (p < 0.05) mold and yeast (Log10 CFU (colony forming unit)/g) counts compared to mixture B. Mixture B and C had higher acetic acid (AA) content than mixture A and D. The lactic acid (LA) content was higher for mixture B than mixture C. In general, the electronic nose (EN) results revealed that the Italian ryegrass and winter cereal mixtures (mixture D) had better aroma profile as compared to winter cereal mixtures (mixture A and B). However, the cereal mixtures (mixture A and B) had better aroma quality than mixture C silage. Otherwise, the EN technology is suitable in finding off odor compounds of ensiled forages.

scholarly journals Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2936 ◽  
Author(s):  
Xianghao Zhan ◽  
Xiaoqing Guan ◽  
Rumeng Wu ◽  
Zhan Wang ◽  
You Wang ◽  
...  
Keyword(s):  
Herbal Medicine ◽  
Electronic Nose ◽  
Nearest Neighbor ◽  
Herbal Medicines ◽  
Principal Component ◽  
Support Vector ◽  
Linear Discriminant ◽  
Convenient Means ◽  
Electronic Nose System ◽  
Insight Into

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

scholarly journals Development of a Piezoelectric-Based Odor Reproduction System

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 870
Author(s):  
Tengteng Wen ◽  
Dehan Luo ◽  
Yongjie Ji ◽  
Pingzhong Zhong
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Piezoelectric Transducers ◽  
Linear Discriminant ◽  
Reproduction System ◽  
Machine Olfaction

Odor reproduction, a branch of machine olfaction, is a technology through which a machine represents various odors by blending several odor sources in different proportions and releases them. In this paper, an odor reproduction system is proposed. The system includes an atomization-based odor dispenser using 16 micro-porous piezoelectric transducers. The authors propose the use of an electronic nose combined with a Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA) model to evaluate the effectiveness of the system. The results indicate that the model can be used to evaluate the system.

scholarly journals Characterization of pasteurized milk spoilage by electronic nose in relation to its selected quality parameters

2021 ◽  
Vol 10 (2) ◽  
pp. 383-397
Author(s):  
Saleem Ehsan ◽  
Zahir Al-Attabi ◽  
Nasser Al-Habsi ◽  
Michel R. G. Claereboudt ◽  
Mohammad Shafiur Rahman
Keyword(s):  
Shelf Life ◽  
Electronic Nose ◽  
Principal Component ◽  
Titratable Acidity ◽  
Bacterial Count ◽  
Quality Parameters ◽  
Accurate Estimation ◽  
Total Bacterial Count ◽  
Microbial Count ◽  
Linear Discriminant

Pasteurized fresh milk requires an accurate estimation of shelf life under various conditions to minimize the risk of spoilage and product losses. Milk samples were stored for 56 h in an oven at 25°C and for 15 days in a refrigerator at 4°C. Samples were analyzed using an electronic nose (e-nose), total bacterial count, titratable acidity and pH to determine the quality of milk. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to analyze e-nose data of milk stored at 25°C, and 4°C. A clear shift in quality was identified by the e-nose, which also appeared in the total bacterial count after 24 h and 12 days for storage at 25 and 4°C, respectively. On the other hand, titratable acidity exceeded the normal limits of 0.14 % - 0.21 % after 24 h for storage at 25°C (0.247 ± 0.006 %) and after 15 days for storage at 4°C (0.25 ± 0.01 %). If pH was a good indicator of quality for samples stored at 25°C, it showed no clear trends for samples stored at 4°C. Based on the microbial count data and e-nose output, the milk had a shelf life of 0.3 day (i.e. 8 h) when stored at 25°C. Shelf life was extended to 9 days when stored at 4°C.

scholarly journals In situ ruminal degradability and fermentation characteristics of novel mixtures of winter cereal and Italian ryegrass plus winter cereal silages

2021 ◽  
Vol 66 (No. 8) ◽  
pp. 302-314
Author(s):  
Alemayehu Worku ◽  
Róbert Tóthi ◽  
Szilvia Orosz ◽  
Hedvig Fébel ◽  
László Kacsala ◽  
...  
Keyword(s):  
Dry Matter ◽  
Lolium Multiflorum ◽  
Control Diet ◽  
Rumen Fermentation ◽  
Italian Ryegrass ◽  
Winter Cereal ◽  
Winter Cereals ◽  
Ruminal Degradability ◽  
Protein Degradability

This study was conducted using three multiparous non-lactating rumen-cannulated Holstein-Friesian dairy cows, with the objective of evaluating the in situ ruminal degradability and fermentation characteristics of novel mixtures of winter cereal and Italian ryegrass (Lolium multiflorum Lam.) plus winter cereal silages (mixture A: triticale, oats, barley and wheat; mixture B: triticale, barley and wheat; mixture C: Italian ryegrass and oats; mixture D: Italian ryegrass, oats, triticale, barley and wheat). The rumen fermentation study was conducted replacing the ensiled mixtures (experimental diets) with vetch-triticale haylage in a total mixed ration (control diet). It was found that the effective protein degradability at 0.08 rumen outflow rates was 80.6% (mixture A), 66.2% (mixture B), 79.7% (mixture C) and 79.3% (mixture D). The effective neutral detergent fibre (NDF) and acid detergent fibre (ADF) effective degradability at 0.08 rumen outflow rates was 18.0% and 17.7% (mixture A), 19.7% and 20.5% (mixture B), 19.1% and 17.0% (mixture C), and 15.2% and 14.6% (mixture D), respectively. Different dietary treatments did not change (P &gt; 0.05) the rumen fermentation characteristics as there was no difference (P &gt; 0.05) between control and experimental diets, and the inclusion of 40–55% Italian ryegrass (mixture C and D) did not cause any difference. These results suggest that the mixture of winter cereals and Italian ryegrass plus winter cereal-based silages had good potentially degradable dry matter, effective dry matter and effective protein degradability at 0.01, 0.05 and 0.08 rumen outflow rates without affecting the rumen environment maintaining neutral pH. The ensiled mixtures had a moderate level of potentially degradable NDF and ADF fractions.

Changes in the Physical and Chemical and Volatile Flavor Characteristics of Scomberomorus niphonius during Chilled and Frozen Storage

2012 ◽  
Vol 573-574 ◽  
pp. 1057-1063
Author(s):  
Bin Zhang ◽  
Shang Gui Deng ◽  
Hui Min Lin
Keyword(s):  
Electronic Nose ◽  
Frozen Storage ◽  
Principal Component ◽  
Textural Properties ◽  
Gompertz Model ◽  
Linear Discriminant ◽  
Dependent Relationship ◽  
Volatile Flavor ◽  
The Right ◽  
Scomberomorus Niphonius

Changes in chemical, textural, and volatile flavor properties were investigated for mackerel fish (Scomberomorus niphonius) stored in cold rooms and freezers. Correlation and multivariate analysis showed a significant time-dependent relationship between TVBN/TMA (Y) and storage time (X) for fish stored in cold rooms, with R=0.996-0.997 values of Gompertz model (Y=a*exp(-exp(b-cX)), and there was a good linear relationship between TVBN and TMA. Combined with the textural properties, the polynomial fitting model (Y=a+bX+cX2+…, R=0.982-0.991) was applied and elucidated the correlation between hardness/springiness (Y) and TVBN (X), the rational function model (Y=(a+bX)/(1+cX+dX2), R=0.975-0.979) used for the chewiness (Y) and TVBN (X). The electronic nose analysis revealed that the variation of muscle volatile flavor compounds was found out along the PC1 to the right, and then along the PC2 to the upward and further to the downward based on the principal component analysis (PCA). Furthermore, the linear discriminant analysis (LDA) had better distinction effect for the changes of fish flavor than PCA. Results from this study suggested that the texture analysis in combination with electronic nose techniques might be utilized as a rapid expeditious process for predicting freshness and shelf life of the alive-storage fish or other aquatic products.

Evaluation of Chinese Vinegar by Electronic Nose

2014 ◽  
Vol 490-491 ◽  
pp. 1497-1502
Author(s):  
Ming Quan Huang ◽  
Jing Lin Zhang ◽  
Ji Hong Wu ◽  
Bao Guo Sun ◽  
Yu Yu Zhang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Linear Discriminant ◽  
Production Area ◽  
Production Areas ◽  
Sulfur Containing ◽  
Chinese Vinegar ◽  
Sulfur Containing Compounds

Twelve vinegars from different production areas in China were evaluated by the Portable Electronic Nose 3 (PEN3), and the data detected by PEN3 were analyzed by principal component analysis (PCA), linear discriminant analysis (LDA) and loadings analysis (LA). The results of PCA and LDA all showed that the electronic nose could clearly discriminate the vinegars of difference production areas, but had very little discrimination on same production area vinegars. The results of LD showed that these sensors, including W1S, W2S, W5S, W2W, W1W, W5C, were appropriate to evaluate and compare the aroma of vinegars, especially W1S, W2S, W5S. Loadings analysis also indicated that these compounds in twelve vinegars had great differences, such as acids, esters, alcohols, alkanes, while aromatics compounds, sulfur-containing compounds and alkenes had some distinctions.

scholarly journals Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM)

2014 ◽  
Vol 32 (No. 6) ◽  
pp. 538-548 ◽  
Author(s):  
A. Sanaeifar ◽  
S.S. Mohtasebi ◽  
M. Ghasemi-Varnamkhasti ◽  
H. Ahmadi ◽  
J. Lozano
Keyword(s):  
Electronic Nose ◽  
Low Cost ◽  
Principal Component ◽  
Oxide Semiconductor ◽  
Support Vector ◽  
Computational Techniques ◽  
Laboratory Equipment ◽  
Linear Discriminant ◽  
Ripening Stages ◽  
Non Destructive

Potential application of a metal oxide semiconductor based electronic nose (e-nose) as a non-destructive instrument for monitoring the change in volatile production of banana during the ripening process was studied. The proposed e-nose does not need any advanced or expensive laboratory equipment and proved to be reliable in recording meaningful differences between ripening stages. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogy (SIMCA) and Support Vector Machines (SVM) techniques were used for this purpose. Results showed that the proposed e-nose can distinguish between different ripening stages. The e-nose was able to detect a clear difference in the aroma fingerprint of banana when using SVM analysis compared with PCA and LDA, SIMCA analysis. Using SVM analysis, it was possible to differentiate and to classify the different banana ripening stages, and this method was able to classify 98.66% of the total samples in each respective group. Sensor array capabilities in the classification of ripening stages using loading analysis and SVM and SIMCA were also investigated, which leads to develop the application of a specific e-nose system by applying the most effective sensors or ignoring the redundant sensors. &nbsp;

scholarly journals ELECTRONIC NOSE FOR ANCHOVY FRESHNESS MONITORING BASED ON SENSOR ARRAY AND PATTERN RECOGNITION METHODS: PRINCIPAL COMPONENTS ANALYSIS, LINEAR DISCRIMINANT ANALYSIS AND SUPPORT VECTOR MACHINE

2014 ◽  
pp. 61-67
Author(s):  
A. Amari ◽  
N. El Bari ◽  
B. Bouchikhi
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Tin Dioxide ◽  
Principal Component ◽  
Support Vector ◽  
Current Pattern ◽  
Linear Discriminant ◽  
Pattern Recognition Methods

An electronic nose based system, which employs an array of six inexpensive commercial gas sensors based on tin dioxide (Figaro Engineering Inc., Japan), has been used to analyse the freshness states of anchovies. Fresh anchovies were stored in a refrigerator at 4 ± 1°C over a period of 15 days. Electronic nose measurements need no sample preparation and the results indicated that the spoilage process of anchovies could be followed by using this technique. Conductance responses of volatile compounds produced during storage of anchovy were monitored and the result were analysed by multivariate analysis methods. In this paper principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate whether the electronic nose was able to distinguishing among different freshness states (fresh, moderated and non-fresh samples). The loadings analysis was used to identify the sensors responsible for discrimination in the current pattern file. Therefore, the support vector machines (SVM) method was applied to the new subset, with only the selected sensors, to confirm that a subset of a few sensors can be chosen to explain all the variance. The results obtained prove that the electronic nose can discriminate successfully different freshness state using LDA analysis. Some sensors have the highest influence in the current pattern file for electronic nose. Support vector machine (SVM) model, applied to the new subset of sensors show the good performance.

scholarly journals Can FT-Mid-Infrared Spectroscopy of Milk Samples Discriminate Different Dietary Regimens of Sheep Grazing With Restricted Access Time?

2021 ◽  
Vol 8 ◽  
Author(s):  
Giovanni Molle ◽  
Andrea Cabiddu ◽  
Mauro Decandia ◽  
Maria Sitzia ◽  
Ignazio Ibba ◽  
...  
Keyword(s):  
Principal Component ◽  
Italian Ryegrass ◽  
Access Time ◽  
Sheep Grazing ◽  
Forage Crops ◽  
Mid Infrared ◽  
Linear Discriminant ◽  
Best Fitting ◽  
Grazing Period ◽  
Mir Spectra

Milk obtained from sheep grazing natural pastures and some forage crops may be worth a plus value as compared to milk obtained from stall-fed sheep, due to their apparently higher content of beneficial fatty acids (FAs). Fourier transformed mid-infrared (FT-MIR) analysis of FA can help distinguish milk from different areas and diverse feeding systems. The objective was to discriminate milk from sheep and milk from dairy sheep rotationally grazing Italian ryegrass or berseem clover for 2, 4, or 6 h/day. To test this hypothesis, a data-mining study was undertaken using a database of 1,230 individual milk spectra. Data were elaborated by principal component analysis (PCA) and analyzed by linear discriminant analysis (LDA) with or without the use of genetic algorithm (GA) as a variable selection tool with the primary aim to discriminate grazed forages (grass vs. legume), access time (2, 4, or 6 h/day), grazing day (first vs. last grazing day during the 7-day grazing period), and the milking time (morning vs. afternoon milking). The best-fitting discriminant models of FT-MIR spectra were able to correctly predict 100% of the samples differing for the pasture forage, 91.9% of the samples differing for grazing day, and 97.1% of the samples regarding their milking time. The access time (AT) to pasture was correctly predicted by the model in 60.3% of the samples, and the classification ability was improved to 77.0% when considering only the 2 and 6 h/day classes.

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