Search

The aim of this study was to evaluate the potential use of an e-nose in combination with lateral flow immunoassays for rapid aflatoxin and fumonisin occurrence/co-occurrence detection in maize samples. For this purpose, 161 samples of corn have been used. Below the regulatory limits, single-contaminated, and co-contaminated samples were classified according to the detection ranges established for commercial lateral flow immunoassays (LFIAs) for mycotoxin determination. Correspondence between methods was evaluated by discriminant function analysis (DFA) procedures using IBM SPSS Statistics 22. Stepwise variable selection was done to select the e-nose sensors for classifying samples by DFA. The overall leave-out-one cross-validated percentage of samples correctly classified by the eight-variate DFA model for aflatoxin was 81%. The overall leave-out-one cross-validated percentage of samples correctly classified by the seven-variate DFA model for fumonisin was 85%. The overall leave-out-one cross-validated percentage of samples correctly classified by the nine-variate DFA model for the three classes of contamination (below the regulatory limits, single-contaminated, co-contaminated) was 65%. Therefore, even though an exhaustive evaluation will require a larger dataset to perform a validation procedure, an electronic nose (e-nose) seems to be a promising rapid/screening method to detect contamination by aflatoxin, fumonisin, or both in maize kernel stocks.

The aim of the present study was to test infrared thermography (IRT), under field conditions, as a possible tool for the evaluation of cow udder health status. Thermographic images (n. 310) from different farms (n. 3) were collected and evaluated using a dedicated software application to calculate automatically and in a standardized way, thermographic indices of each udder. Results obtained have confirmed a significant relationship between udder surface skin temperature (USST) and classes of somatic cell count in collected milk samples. Sensitivity and specificity in the classification of udder health were: 78.6% and 77.9%, respectively, considering a level of somatic cell count ( SCC ) of 200,000 cells/mL as a threshold to classify a subclinical mastitis or 71.4% and 71.6%, respectively when a threshold of 400,000 cells/mL was adopted. Even though the sensitivity and specificity were lower than in other published papers dealing with non-automated analysis of IRT images, they were considered acceptable as a first field application of this new and developing technology. Future research will permit further improvements in the use of IRT, at farm level. Such improvements could be attained through further image processing and enhancement, and the application of indicators developed and tested in the present study with the purpose of developing a monitoring system for the automatic and early detection of mastitis in individual animals on commercial farms., Competing Interests: The authors declare no conflict of interest.

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., Competing Interests: The authors declare no conflict of interest.

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., Competing Interests: The authors declare no conflict of interest.

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., Competing Interests: The authors declare no conflict of interest.

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.

Mycotoxins represent a risk to the feed supply chain with an impact on economies and international trade. A high percentage of feed samples have been reported to be contaminated with more than one mycotoxin. In most cases, the concentrations were low enough to ensure compliance with the European Union (EU) guidance values or maximum admitted levels. However, mycotoxin co-contamination might still exert adverse effects on animals due to additive/synergistic interactions. Studies on the fate of mycotoxins during cereal processing, such as milling, production of ethanol fuels, and beer brewing, have shown that mycotoxins are concentrated into fractions that are commonly used as animal feed. Published data show a high variability in mycotoxin repartitioning, mainly due to the type of mycotoxins, the level and extent of fungal contamination, and a failure to understand the complexity of food processing technologies. Precise knowledge of mycotoxin repartitioning during technological processes is critical and may provide a sound technical basis for feed managers to conform to legislation requirements and reduce the risk of severe adverse market and trade repercussions. Regular, economical and straightforward feed testing is critical to reach a quick and accurate diagnosis of feed quality. The use of rapid methods represents a future challenge.

Electronic Olfaction Systems (EOSs) based on a variety of gas-sensing technologies have been developed to simulate in a simplified manner animal olfactory sensing systems. EOSs have been successfully applied to many applications and fields, including food technology and agriculture. Less information is available for EOS applications in the feed technology and animal nutrition sectors. Volatile Organic Compounds (VOCs), which are derived from both forages and concentrate ingredients of farm animal rations, are considered and described in this review as olfactory markers for feedstock quality and safety evaluation. EOS applications to detect VOCs from feedstuffs (as analytical matrices) are described, and some future scenarios are hypothesised. Furthermore, some EOS applications in animal feeding behaviour and organoleptic feed assessment are also described.

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).