Convolutional Neural Networks for Olive Oil Classification

The analysis of the quality of olive oil is a task that is hav-ing a lot of impact nowadays due to the large frauds that have been observed in the olive oil market. To solve this problem we have trained a Convolutional Neural Network (CNN) to classify 701 images obtained using GC-IMS methodology (gas chromatography coupled to ion mobil-ity spectrometry). The aim of this study is to show that Deep Learn-ing techniques can be a great alternative to traditional oil classification methods based on the subjectivity of the standardized sensory analy-sis according to the panel test method, and also to novel techniques provided by the chemical field, such as chemometric markers. This tech-nique is quite expensive since the markers are manually extracted by an expert. The analyzed data includes instances belonging to two different crops, the first covers the years 2014–2015 and the second 2015–2016. Both har-vests have instances classified in the three categories of existing oil, extra virgin olive oil (EVOO), virgin olive oil (VOO) and lampante olive oil (LOO). The aim of this study is to demonstrate that Deep Learning techniques in combination with chemical techniques are a good alterna-tive to the panel test method, implying even better accuracy than results obtained in previous wor

Alignment of retention time obtained from multicapillary column gas chromatography used for VOC analysis with ion mobility spectrometry

Multicapillary column (MCC) ion mobility spectrometers (IMS) are increasingly in demand for medical diagnosis, biological applications and process control. In a MCC-IMS, volatile compounds are differentiated by specific retention time and ion mobility when rapid preseparation techniques are applied, e.g. for the analysis of complex and humid samples. Therefore, high accuracy in the determination of both parameters is required for reliable identification of the signals. The retention time in the MCC is the subject of the present investigation because, for such columns, small deviations in temperature and flow velocity may cause significant changes in retention time. Therefore, a universal correction procedure would be a helpful tool to increase the accuracy of the data obtained from a gas-chromatographic preseparation. Although the effect of the carrier gas flow velocity and temperature on retention time is not linear, it could be demonstrated that a linear alignment can compensate for the changes in retention time due to common minor deviations of both the carrier gas flow velocity and the column temperature around the MCC-IMS standard operation conditions. Therefore, an effective linear alignment procedure for the correction of those deviations has been developed from the analyses of defined gas mixtures under various experimental conditions. This procedure was then applied to data sets generated from real breath analyses obtained in clinical studies using different instruments at different measuring sites for validation. The variation in the retention time of known signals, especially for compounds with higher retention times, was significantly improved. The alignment of the retention time—an indispensable procedure to achieve a more precise identification of analytes—using the proposed method reduces the random error caused by small accidental deviations in column temperature and flow velocity significantly

Prevalence of anxiety, depression and post-traumatic stress disorder among Ebola survivors in northern Sierra Leone: a cross-sectional study.

BACKGROUND:There is limited data available on the long-term mental health impact of Ebola virus disease (EVD) on survivors despite the disease experience of survivors meeting the criteria of a traumatic event as defined in the Diagnostic and Statistical Manual of Mental Disorders version IV (DSM IV). This study aimed to assess the prevalence and predictive factors of anxiety, depression and posttraumatic stress disorder among EVD survivors, approximately 2 years after discharge from the Ebola treatment centre (ETC). METHODS:We conducted a cross-sectional study between May and August 2017 among 197 adults Ebola survivors in Bombali district, Northern Sierra Leone. We collected information about demographics, mental health status and possible predictive factors. The HAD scale was used to measure anxiety and depression. PTSD was measured using the PTSD-checklist (PCL). Chi-square test or Fisher exact two-tailed tests were used to test for associations and the multiple logistic regressions model to determine factors that were independently associated with the outcome variables. RESULTS:The mean anxiety, depression and PTSD scores were (5.0 ± 3.9), (7.1 ± 3.8) and (39.5 ± 6.4) respectively. Based on cut-off scores, the prevalence of anxiety (HADs score ≥ 8), depression (HADs score ≥ 8) and PTSD (PCL ≥ 45) among Ebola survivors were (n = 49, 24.9%), (n = 93, 47.2%) and (n = 43, 21.8%) respectively. Older Ebola survivors (≥30 years) were more likely to show symptoms of depression (AOR = 8.5, 95% CI: 2.68-27.01, p = 0.001) and anxiety (AOR = 3.04; 95%CI: 1.2-7.7, p = 0.019) compared to younger ones (< 30 years). In addition, Ebola survivors who experienced a decreased level of exercise post-ETC discharge were more likely to show symptoms of depression (AOR = 2.63; 95%CI: 1.25-5.54, p = 0.011) and anxiety (AOR = 3.60; 95%CI: 1.33-9.72, p = 0.012) compared to those whose exercise remained the same post-ETC discharge. CONCLUSION:Our findings show that anxiety, depression and PTSD are common among the Ebola survivors in Bombali district, Northern Sierra Leone, and that underscores the need to diagnose and manage mental health morbidities among Ebola survivors long after their recovery from Ebola virus disease. Cognitive Behaviour Therapy (CBT) and Interpersonal Therapy (IPT) need to be explored as part of overall mental healthcare package interventions

Pesticide Importation in Sierra Leone, 2010-2021: Implications for Food Production and Antimicrobial Resistance.

There are no previous studies reporting the type and quantity of pesticides for farming from Sierra Leone and the impact of Ebola or COVID-19 on importation. This study reviewed imported farming pesticides by the Sierra Leone, Ministry of Agriculture and Forestry (MAF), between 2010-2021. It was a descriptive study using routinely collected importation data. We found the MAF imported pesticides for farming only during 2010, 2014 and 2021, in response to growing food insecurity and associated with Ebola and COVID-19 outbreaks. Results showed insecticide importation increased from 6230 L in 2010 to 51,150 L in 2021, and importation of antimicrobial pesticides (including fungicides) increased from 150 kg in 2010 to 23,560 kg in 2021. The hazard class risk classification of imported pesticides decreased over time. Increasing amounts of imported fungicides could increase the risk of future fungal resistance among humans. We found that in responding to escalating food insecurity, the government dramatically increased the amount of pesticide importation to improve crop production. Further support is necessary to decrease the risk of worsening food shortages and the possible threat of emerging antimicrobial resistance. We recommend continued monitoring and surveillance, with further studies on the most appropriate response to these multiple challenges