Michael Fernandes, Dipl.-Phys.

Wissenschaftlicher Mitarbeiter

Grafenau

08552/975620-44


Zeitschriftenartikel
  • S. Goisser
  • S. Wittmann
  • Michael Fernandes
  • H. Mempel
  • C. Ulrichs
Comparison of colorimeter and different portable food-scanners for non-destructive prediction of lycopene content in tomato fruit, vol. 167, pg. 111232.

In: Postharvest Biology and Technology

  • 2020

DOI: 10.1016/j.postharvbio.2020.111232

Lycopene, the red colored carotenoid in tomatoes, has various health benefits for humans due to its capability of scavenging free radicals. Traditionally, the quantification of lycopene requires an elaborate extraction process combined with HPLC analysis within the laboratory. Recent studies focused simpler methods for determining lycopene and utilized spectroscopic measurement methods. The aim of this study was to compare non-destructive methods for the prediction of lycopene by using color values from colorimeter measurements and Vis/NIR spectra recorded with three commercially available and portable Vis/NIR spectrometers, so called food-scanners. Tomatoes of five different ripening stages (green to red) as well as tomatoes stored up to 22 days after harvest were used for modeling. After measurement of color values and collection of Vis/NIR spectra the corresponding lycopene content was analyzed spectrophotometrically. Applying exponential regression models yielded very good prediction of lycopene for color values L*, a*, a*/b* and the tomato color index of 0.94, 0.90, 0.90 and 0.91, respectively. Color value b* was not a suitable predictor for lycopene content, whereas the (a*/b*)² value had the best linear fit of 0.87. In comparison to color measurements, the cross-validated prediction models developed for all three food-scanners had coefficients of determination (r²CV) ranging from 0.92 to 0.96. Food-scanners also can be used for additional measurements of internal fruit quality, and therefore have great potential for fruit quality assessment by measuring a multitude of important fruit traits in one single scan.
  • TC Grafenau
  • DIGITAL
  • NACHHALTIG
Beitrag (Sammelband oder Tagungsband)
  • S. Goisser
  • J. Krause
  • Michael Fernandes
  • H. Mempel
Determination of tomato quality attributes using portable NIR-sensors, pg. 1-12.
  • 2019

DOI: 10.5445/KSP/1000087509

As part of a research project a multidisciplinary approach of different research institutes is followed to investigate the possibility of using a commercially available miniaturized NIR-sensor for the determination of tomato fruit quality parameters in postharvest. Correlation of spectra and tomato reference values of firmness, dry matter and total soluble solids showed good prediction accuracy. Additionally the decline of firmness over storage time with respect to storage temperature of tomatoes could be modelled. Therefore, the decline of firmness as an indicator for shelf-life can be predicted using this portable NIR-Sensor.
  • Angewandte Wirtschaftswissenschaften
  • TC Grafenau
  • NACHHALTIG
  • DIGITAL
Vortrag
  • Michael Fernandes
  • Florian Wahl
Tomatenkrimi - Der Foodscanner als Ermittler. Keynote

In: Tag der offenen Türe des Technologie Campus Grafenau

  • 2019
  • TC Grafenau
  • Angewandte Informatik
  • NACHHALTIG
Beitrag (Sammelband oder Tagungsband)
  • S. Goisser
  • Michael Fernandes
  • C. Ulrichs
  • H. Mempel
Non-destructive measurement method for a fast quality evaluation of fruit and vegetables by using food-scanner, pg. 1-5.
  • 2018

DOI: 10.5288/dgg-pr-sg-2018

Recent reports estimate the volume of food loss along the supply chain to 1,3 billion tons globally per year, which equals one-third of food produced for human consumption (FAO, 2011). Further studies conducted for the German food supply chain estimate the quantity of annual food loss between 11 million (Universität Stuttgart, 2012) and 18 million (WWF, 2015) tons. Fruits and vegetables, with a percentage of 44 of the total food loss, are commodities most frequently thrown away (BMEL, 2012). In recent years, a lot of attention is given to so-called food-scanners. Food-scanner are miniaturized near-infrared (NIR) spectrometers, which allow a fast and noninvasive determination of food quality. They can be used as a multidimensional predictor to determine the chemical and physical composition of agricultural and food products (e.g. soluble solids, dry matter, moisture, firmness). Due to their small size and portability these devices can be used for in-field application as well as for researchers andend-consumers (Santos et al.,2013). Studies of Flores et al. (2009) and Kim et al. (2013) indicate that NIRS is suitable for predicting quality attributes of various tomato varieties. The experiments described in this study are conducted on tomatoes and focus on the performance of a food-scanner compared to a benchtop NIR-spectrometer. Important quality parameters of tomato, such as sugar content and firmness, are evaluated with respect to their predictability in order to validate the performance of this new kind of non-destructive measurement method.
  • TC Grafenau
  • NACHHALTIG
  • DIGITAL
Vortrag
  • Michael Fernandes
Food-Scanner: Lebensmittelqualität einfach bestimmen. Posterpräsentation

In: 5. Tag der Forschung

  • 2018
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
  • NACHHALTIG
Beitrag (Sammelband oder Tagungsband)
  • S. Goisser
  • Michael Fernandes
  • H. Mempel
Zerstörungsfreie Messmethode zur schnellen Qualitätsbewertung und Haltbarkeitsabschätzung von Lebensmitteln mit Hilfe von Food Scannern, pg. 34.

In: 52. Gartenbauwissenschaftliche Jahrestagung „Klimafolgen und Herausforderungen für den Gartenbau“. null (BHGL-Schriftenreihe)

  • 2018
  • TC Grafenau
  • NACHHALTIG
  • DIGITAL
Zeitschriftenartikel
  • Nari Arunraj
  • Robert Hable
  • Michael Fernandes
  • Karl Leidl
  • Michael Heigl
Comparison of Supervised, Semi-supervised and Unsupervised Learning Methods in Network Intrusion Detection Systems (NIDS) Application, pg. 10-19.

In: Anwendungen und Konzepte in der Wirtschaftsinformatik (AKWI)

  • 2017
With the emergence of the fourth industrial revolution (Industrie 4.0) of cyber physical systems, intrusion detection systems are highly necessary to detect industrial network attacks. Recently, the increase in application of specialized machine learning techniques is gaining critical attention in the intrusion detection community. A wide variety of learning techniques proposed for different network intrusion detection system (NIDS) problems can be roughly classified into three broad categories: supervised, semi-supervised and unsupervised. In this paper, a comparative study of selected learning methods from each of these three kinds is carried out. In order to assess these learning methods, they are subjected to investigate network traffic datasets from an Airplane Cabin Demonstrator. In addition to this, the imbalanced classes (normal and anomaly classes) that are present in the captured network traffic data is one of the most crucial issues to be taken into consideration. From this investigation, it has been identified that supervised learning methods (logistic and lasso logistic regression methods) perform better than other methodswhen historical data on former attacks are available. The results of this study have also showed that the performance of semi-supervised learning method (One class support vector machine) is comparatively better than unsupervised learning method (Isolation Forest) when historical data on former attacks are not available.
  • TC Teisnach Sensorik
  • TC Grafenau
  • Institut ProtectIT
  • DIGITAL
Vortrag
  • Michael Fernandes
Das Auge analysiert mit - Datenanalyse und Visualisierung

In: 3. Tag der Forschung - Themenbereiche Wirtschaft und Gesundheit

  • 2016
  • TC Grafenau
  • Angewandte Informatik
Zeitschriftenartikel
  • Nari Arunraj
  • Diane Ahrens
  • Michael Fernandes
Application of SARIMAX model to forecast daily sales in retail industry, vol. 7, pg. 1-20.

In: International Journal of Operations Research and Information Systems (IJORIS)

  • 2016

DOI: 10.4018/IJORIS.2016040101

Abstract During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Article Preview 1. Introduction Discount retail stores have been a noticeable feature of German retail market since the 1980s. In particular, the growth in number of discount retail stores have significantly increased after reunification of Germany. Recently, there is a growing trend of increasing varieties of fruits and vegetables with year-around availability across all the German discount retail outlets rather than just in their traditional growing season. In order to attract customers and remain competitive in the market, the fruits and vegetables are exported from foreign countries and stocked for longer periods. Particularly, increase in number of retail stores, availability of varieties of fruits and vegetables (in stock) with short shelf-lives, frequent price variations, and different storage conditions increase the complexity and results in huge amount of food waste. In Germany, the retail sector produces the food waste of around 0.5 million tons per year (Kranert et al., 2012). Although the retail sector contributes only 5% of the total food waste in food supply chain, mostly they are avoidable food waste (wasting food which is fit for consumption). The quantity of food waste that occurs in the home (61%) is partially due to the management decisions in the retail sector (e.g. frequent promotions) that stimulate the consumer’s eagerness to purchase, and distract them to equate their demand with the purchase (Arunraj et al., 2014; Gooch et al., 2010). Hence, the proper decision making in the retail sector can help the suppliers and consumers to avoid the food waste. The role of sales forecasting in reducing the food waste in retail stores is a significant topic of discussion in the recent food waste related studies (Mena et al., 2011; Mena et al., 2014). According to Mena et al. (2011) and Stenmarck et al. (2011), the improvement of forecast accuracy is one of the essential remedial measures to reduce the food waste in the retail sector of food supply chain.
  • TC Grafenau
  • DIGITAL
Vortrag
  • Michael Fernandes
Intelligentes Prognose- und Dispositionsverfahren im Lebensmitteleinzelhandel

In: EssensWert Fachtagung

  • 2014
  • TC Grafenau
  • Angewandte Informatik
Beitrag (Sammelband oder Tagungsband)
  • Nari Arunraj
  • Diane Ahrens
  • Michael Fernandes
  • M. Müller
Time series sales forecasting to reduce food waste in retail industry
  • 2014
  • TC Grafenau
Vortrag
  • Nari Arunraj
  • Diane Ahrens
  • Michael Fernandes
  • Martin Müller
Time series sales forecasting to reduce food waste in retail industry

In: 34th International Symposium on Forecasting

  • 2014
  • Angewandte Informatik
  • TC Grafenau