Mauro Zaninelli

Use of the electronic nose as a screening tool for the recognition of durum wheat naturally contaminated by deoxynivalenol: a preliminary approach.

Fungal contamination and the presence of related toxins is a widespread problem. Mycotoxin contamination has prompted many countries to establish appropriate tolerance levels. For instance, with the Commission Regulation (EC) N. 1881/2006, the European Commission fixed the limits for the main mycotoxins (and other contaminants) in food. Although valid analytical methods are being developed for regulatory purposes, a need exists for alternative screening methods that can detect mould and mycotoxin contamination of cereal grains with high sample throughput. In this study, a commercial electronic nose (EN) equipped with metal-oxide-semiconductor (MOS) sensors was used in combination with a trap and the thermal desorption technique, with the adoption of Tenax TA as an adsorbent material to discriminate between durum wheat whole-grain samples naturally contaminated with deoxynivalenol (DON) and non-contaminated samples. Each wheat sample was analysed with the EN at four different desorption temperatures (i.e., 180 °C, 200 °C, 220 °C, and 240 °C) and without a desorption pre-treatment. A 20-sample and a 122-sample dataset were processed by means of principal component analysis (PCA) and classified via classification and regression trees (CART). Results, validated with two different methods, showed that it was possible to classify wheat samples into three clusters based on the DON content proposed by the European legislation: (a) non-contaminated; (b) contaminated below the limit (DON < 1,750 μg/kg); (c) contaminated above the limit (DON > 1,750 μg/kg), with a classification error rate in prediction of 0% (for the 20-sample dataset) and 3.28% (for the 122-sample dataset).

Echocardiographic measurements of cardiac dimensions in normal Standardbred racehorses

OBJECTIVES The aim of this study was to obtain echocardiographic measurements and establish reference ranges for 14 parameters in Standardbred racehorses in training. BACKGROUND Several studies have been published about cardiac measurements in Thoroughbreds, Standardbreds, National Hunt horses, Warmbloods and ponies; however, not all parameters have been published for the Standardbred trotter in training. ANIMALS, MATERIALS AND METHODS Thirty normal Standardbred racehorses in training were assessed by two-dimensional echocardiography (2-D) and M-mode echocardiography using standardized imaging planes. Mean values, standard deviations, 95% confidence interval for the means and 95% confidence interval for the cardiac parameters measured in the population were calculated. Furthermore, a general linear model was constructed using sex, age and body weight (bwt) of the horses as independent variables and the echocardiographic measurements as dependent variables. Multivariate linear regression analysis was performed with the level of significance at p<0.05 for all the null hypotheses. RESULTS Reference ranges were established for 14 echocardiographic parameters in Standardbred racehorses. Weak linear relationships between echocardiographic measurements and body weight were observed for LVIDd, LVIDs, LVFWs, and AOD. Linear regressions on these parameters were used to calculate the 95% confidence intervals for the predicted values. CONCLUSIONS The data collected in this study provide reference values for the evaluation of Standardbred racehorses in training. Body weight has a negligible affect on most echocardiographic parameters in this homogeneous population, but did mildly influence the results of left ventricular and aortic measurements.

Evaluation of the Fourier Frequency Spectrum Peaks of Milk Electrical Conductivity Signals as Indexes to Monitor the Dairy Goats’ Health Status by On-Line Sensors

The aim of this study is a further characterization of the electrical conductivity (EC) signal of goat milk, acquired on-line by EC sensors, to identify new indexes representative of the EC variations that can be observed during milking, when considering not healthy (NH) glands. Two foremilk gland samples from 42 Saanen goats, were collected for three consecutive weeks and for three different lactation stages (LS: 0–60 Days In Milking (DIM); 61–120 DIM; 121–180 DIM), for a total amount of 1512 samples. Bacteriological analyses and somatic cells counts (SCC) were used to define the health status of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as NH. For each milk EC signal, acquired on-line and for each gland considered, the Fourier frequency spectrum of the signal was calculated and three representative frequency peaks were identified. To evaluate data acquired a MIXED procedure was used considering the HS, LS and LS × HS as explanatory variables in the statistical model.Results showed that the studied frequency peaks had a significant relationship with the gland’s health status. Results also explained how the milk EC signals’ pattern change in case of NH glands. In fact, it is characterized by slower fluctuations (due to the lower frequencies of the peaks) and by an irregular trend (due to the higher amplitudes of all the main frequency peaks). Therefore, these frequency peaks could be used as new indexes to improve the performances of algorithms based on multivariate models which evaluate the health status of dairy goats through the use of gland milk EC sensors.

On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

Intramammary infection affects the quality and quantity of dairy goat milk. Health status (HS) and milk quality can be monitored by electrical conductivity (EC). The aim of the study was to determine the detection potential of EC when measured on-line on a daily basis and compared with readings from previous milkings. Milk yields (MYs) were investigated with the same approach. To evaluate these relative traits, a multivariate model based on fuzzy logic technology – which provided interesting results in cows – was used. Two foremilk samples from 8 healthy Saanen goats were measured daily over the course of six months. Bacteriological tests and somatic cells counts were used to define the HS. On-line EC measurements for each gland and MYs were also considered. Predicted deviations of EC and MY were calculated using a moving-average model and entered in the fuzzy logic model. The reported accuracy has a sensitivity of 81% and a specificity of 69%. Conclusions show that fuzzy logic is an interesting approach for dairy goats, since it offered better accuracy than other methods previously published. Nevertheless, specificity was lower than in dairy cows, probably due to the lack of a significant decrease of MY in diseased glands. Still, results show that the detection of the HS characteristics with EC is improved, when measured on-line, daily and compared with the readings from previous milkings.

Signal spectral analysis to characterize gland milk electrical conductivity in dairy goats

Intramammary infection affects quality and quantity of milk. Having as final target the improving of animal health’ monitoring, this research studied the gland milk electrical conductivity (EC) signal in order to identify a possible parameter more representative of the EC variations that can be observed, during a milking, when not healthy (NH) glands are considered. Two foremilk gland samples, from 40 Saanen goats, were acquired for three weeks and lactation stages (LS: 0-60 Days In Milking; 61-120 DIM; =>120 DIM), for a total amount of 1440 samples. Bacteriological analyses and somatic cells counts (SCC) were used to define glands health status. In case of negative bacteriological analyses and SCC <1,000,000 cells/mL, glands were classified as healthy; alternatively, when bacteriological analyses were positive or SCC higher than 1,000,000 cells/mL, for two or more consecutive days, glands were classified as NH. A spectral analysis, to calculate the frequency spectrum and the bandwidth length of the milk EC signal, was performed. To validate data acquired, A MIXED procedure was used considering the HS, LS and the LS x HS as explanatory variables of the statistical model. Results showed that spectral analysis allows characterizing the milk EC variations thorough the bandwidth length parameter. This parameter has a significant relationship with the gland health status and it provides more accurate information than other traits, like the statistical variance of the signal. Therefore, it could be useful to improve the performances of multivariate models/algorithms that detect dairy goat health status.

First Results of a Detection Sensor for the Monitoring of Laying Hens Reared in a Commercial Organic Egg Production Farm Based on the Use of Infrared Technology.

The development of a monitoring system to identify the presence of laying hens, in a closed room of a free-range commercial organic egg production farm, was the aim of this study. This monitoring system was based on the infrared (IR) technology and had, as final target, a possible reduction of atmospheric ammonia levels and bacterial load. Tests were carried out for three weeks and involved 7 ISA (Institut de Sélection Animale) brown laying hens. The first 5 days was used to set up the detection sensor, while the other 15 days were used to evaluate the accuracy of the resulting monitoring system, in terms of sensitivity and specificity. The setup procedure included the evaluation of different color background (CB) thresholds, used to discriminate the information contents of the thermographic images. At the end of this procedure, a CB threshold equal to an increase of 3 °C from the floor temperature was chosen, and a cutoff level of 196 colored pixels was identified as the threshold to use to classify a positive case. The results of field tests showed that the developed monitoring system reached a fine detection accuracy (sensitivity = 97.9% and specificity = 94.9%) and the IR technology proved to be a possible solution for the development of a detection sensor necessary to reach the scope of this study.

Development of HW and SW solutions for milk traceability

Fontina cheese is a DOP (Designation of Protected Origin) cheese produced in Aosta Valley (Western Alps, North Italy) by dairy farms and cheese factories members of Fontina Cheese Producers’ Consortium. Since 2003 Fontina Consortium has developed an identification and branding system to trace the cheese from the factories to the consumers, but till now no operative instruments have been developed for the traceability of the milk produced by the farms members of the Consortium. This lack is mainly due to the traditional dairy cattle breeding system of Aosta Valley that is based on small family-operated dairy farms characterized by a low technological level. In these farms cows are housed in stall barns during winter, while in spring and summer livestock are herded to upland pastures located at the highest elevations (up to 2300 m a.s.l.). The average number of lactating cows is lower than 20 and they are milked by mobile milking units, bucket or pipeline milking systems. Following the European Union Regulation (EC Reg. 178/2002), a traceability system of the milk was developed for the dairy farms located in mountain areas. The project has been articulated in three sequential phases: • development of a database model (Entity and Relationship model) to identify all the farm data (livestock treatments, feed registration, external foodstuff records, etc) required and useful to trace the milk yield; • implementation of the database model by a user friendly software application to simplify the storage and the management of all the farm data; • development of a special mobile milking unit to record milking data (cows production and milk electric conductivity). The mobile milking unit was equipped with: a RFID antenna to identify the cows by transponders (ruminal bolus); a mastitis detection device, based on the measurement of the milk electrical conductivity, to monitor the quality of the milk produced by each cow; a milk meter to measure the milk yield of each cow; a palm PC with a customized software application to record milking data and then transfer them to the farm database. Presently a field test of the developed system is in progress in a farm in Aosta Valley. Preliminary results showed a fairly good effectiveness of the system in tracing milk production from the stable to the dairy factory nailing the traceability system implemented by Fontina Consortium.

A Monitoring System for Laying Hens That Uses a Detection Sensor Based on Infrared Technology and Image Pattern Recognition

In Italy, organic egg production farms use free-range housing systems with a big outdoor area and a flock of no more than 500 hens. With additional devices and/or farming procedures, the whole flock could be forced to stay in the outdoor area for a limited time of the day. As a consequence, ozone treatments of housing areas could be performed in order to reduce the levels of atmospheric ammonia and bacterial load without risks, due by its toxicity, both for hens and workers. However, an automatic monitoring system, and a sensor able to detect the presence of animals, would be necessary. For this purpose, a first sensor was developed but some limits, related to the time necessary to detect a hen, were observed. In this study, significant improvements, for this sensor, are proposed. They were reached by an image pattern recognition technique that was applied to thermografic images acquired from the housing system. An experimental group of seven laying hens was selected for the tests, carried out for three weeks. The first week was used to set-up the sensor. Different templates, to use for the pattern recognition, were studied and different floor temperature shifts were investigated. At the end of these evaluations, a template of elliptical shape, and sizes of 135 × 63 pixels, was chosen. Furthermore, a temperature shift of one degree was selected to calculate, for each image, a color background threshold to apply in the following field tests. Obtained results showed an improvement of the sensor detection accuracy that reached values of sensitivity and specificity of 95.1% and 98.7%. In addition, the range of time necessary to detect a hen, or classify a case, was reduced at two seconds. This result could allow the sensor to control a bigger area of the housing system. Thus, the resulting monitoring system could allow to perform the sanitary treatments without risks both for animals and humans.

Improved Fuzzy Logic System to Evaluate Milk Electrical Conductivity Signals from On-Line Sensors to Monitor Dairy Goat Mastitis.

The aim of this study was to develop and test a new fuzzy logic model for monitoring the udder health status (HS) of goats. The model evaluated, as input variables, the milk electrical conductivity (EC) signal, acquired on-line for each gland by a dedicated sensor, the bandwidth length and the frequency and amplitude of the first main peak of the Fourier frequency spectrum of the recorded milk EC signal. Two foremilk gland samples were collected from eight Saanen goats for six months at morning milking (lactation stages (LS): 0–60 Days In Milking (DIM); 61–120 DIM; 121–180 DIM), for a total of 5592 samples. Bacteriological analyses and somatic cell counts (SCC) were used to define the HS of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as not healthy (NH). For each EC signal, an estimated EC value was calculated and a relative deviation was obtained. Furthermore, the Fourier frequency spectrum was evaluated and bandwidth length, frequency and amplitude of the first main peak were identified. Before using these indexes as input variables of the fuzzy logic model a linear mixed-effects model was developed to evaluate the acquired data considering the HS, LS and LS × HS as explanatory variables. Results showed that performance of a fuzzy logic model, in the monitoring of mammary gland HS, could be improved by the use of EC indexes derived from the Fourier frequency spectra of gland milk EC signals recorded by on-line EC sensors.

Development of a Machine Vision Method for the Monitoring of Laying Hens and Detection of Multiple Nest Occupations

Free range systems can improve the welfare of laying hens. However, the access to environmental resources can be partially limited by social interactions, feeding of hens, and productivity, can be not stable and damaging behaviors, or negative events, can be observed more frequently than in conventional housing systems. In order to reach a real improvement of the hens’ welfare the study of their laying performances and behaviors is necessary. With this purpose, many systems have been developed. However, most of them do not detect a multiple occupation of the nest negatively affecting the accuracy of data collected. To overcome this issue, a new “nest-usage-sensor” was developed and tested. It was based on the evaluation of thermografic images, as acquired by a thermo-camera, and the performing of patter recognitions on images acquired from the nest interior. The sensor was setup with a “Multiple Nest Occupation Threshold” of 796 colored pixels and a template of triangular shape and sizes of 43 × 33 pixels (high per base). It was tested through an experimental nesting system where 10 hens were reared for a month. Results showed that the evaluation of thermografic images could increase the detection performance of a multiple occupation of the nest and to apply an image pattern recognition technique could allow for counting the number of hens in the nest in case of a multiple occupation. As a consequence, the accuracy of data collected in studies on laying performances and behaviors of hens, reared in a free-range housing system, could result to be improved.