Development of an alcohol fermentation control system based on biosensor measurements interpreted by neural networks

Ferreira, L.S.; Souza, Maurício B. de; Folly, Rossana O. M.

doi:10.1016/s0925-4005(01)00540-8

Cited by 27 publications

(8 citation statements)

References 15 publications

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“…Finally, biosensors have been also used in Food Technology to monitor the quality control of raw materials and on-line or off-line manufacturing processes. Thus, glucose and sucrose biosensors have been used to control the process of alcohol fermentation (Ferreira et al 2001), and to detect enterotoxins (Tempelman et al 1996) and bacteria (Su et al 2001) in many foodstuffs. Recently, a biosensor based on catalase enzyme has been developed for hydroperoxide determination in extra virgin olive oil samples (Campanella et al 2001).…”

Section: Biosensorsmentioning

confidence: 99%

Sensors: From biosensors to the electronic nose

García‐González

Aparicio

2002

Grasas y Aceites

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Section: Biosensorsmentioning

confidence: 99%

Sensors: From biosensors to the electronic nose

García‐González

Aparicio

2002

Grasas y Aceites

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“…In this way, a juice is produced which contains less insoluble material (fibre debris) in order to minimize interference in the biosensor response and bacterial contamination during transport of the sugarcane from the field. Ferreira et al 17 reported the use of a cheap and robust biosensor for sucrose and glucose, applied in alcohol fermentation processes for measurement of real analyte concentration. However, their system was a biosensor, modeled using typical experimental data interpreted by neural networks, in the control of alcohol fed‐batch fermentation.…”

Section: Resultsmentioning

confidence: 99%

Sucrose biosensor as an alternative tool for sugarcane field samples

et al. 2007

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The goal of this preliminary study was to produce an alternative tool for measuring the amount of sucrose in samples without pre-treatment and use it in the sugarcane field or mobile units. The technology developed makes payment for the sugarcane much more reliable to the producer and also can improve the quality control of the final product. A flow injection analysis system for measuring sucrose using invertase (E.C. 3.2.1.26), and glucose oxidase (E.C.1.1.3.4) (GOD) was developed. The biosensor was made using invertase and GOD incorporated into active graphite paste mixed with tetracyanoquinodimethane as mediator. The system was able to measure sucrose from 50 up to 200 mmol L −1 with a response time of 5 min using 400 µL of sample in 100 mmol L −1 phosphate buffer pH 6.5. This sucrose biosensor showed a sensitivity of 174 ± 8.7 nA mg −1 of sucrose, being a robust system to measure high concentrations of sucrose in sugarcane crops and industrial and fermentation processes, without dilution or pre-treatment of the samples. Interference from organic acids, mainly ascorbic acid (+350 mV versus Ag/AgCl), was reduced in this system using the potential of 100 mV vs. Ag/AgCl. The active electrode was stable during 2 months at 4• C.

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“…Typical examples are ANNS used for the detection of glucose and sucrose [18], phenolic compounds [19–21], and neuroactive compounds [22]. In direct association with the present study, ANNs have been used in the biosensor-based determination of various insecticides, such as paraoxon (organophosphate) and carbofuran (carbamate) [23], as well as a series of organophosphate pesticides such as chlorpyrifos-oxon, chlorfenvinphos and azinphos-methyl oxon [24–26], dipterex, dichlorvos, and omethoate [27].…”

Section: Discussionmentioning

confidence: 99%

Pesticide Residue Screening Using a Novel Artificial Neural Network Combined with a Bioelectric Cellular Biosensor

Ferentinos

Yialouris

Blouchos

et al. 2013

BioMed Research International

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We developed a novel artificial neural network (ANN) system able to detect and classify pesticide residues. The novel ANN is coupled, in a customized way, to a cellular biosensor operation based on the bioelectric recognition assay (BERA) and able to simultaneously assay eight samples in three minutes. The novel system was developed using the data (time series) of the electrophysiological responses of three different cultured cell lines against three different pesticide groups (carbamates, pyrethroids, and organophosphates). Using the novel system, we were able to classify correctly the presence of the investigated pesticide groups with an overall success rate of 83.6%. Considering that only 70,000–80,000 samples are annually tested in Europe with current conventional technologies (an extremely minor fraction of the actual screening needs), the system reported in the present study could contribute to a screening system milestone for the future landscape in food safety control.

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Development of an alcohol fermentation control system based on biosensor measurements interpreted by neural networks

Cited by 27 publications

References 15 publications

Sensors: From biosensors to the electronic nose

Sensors: From biosensors to the electronic nose

Sucrose biosensor as an alternative tool for sugarcane field samples

Pesticide Residue Screening Using a Novel Artificial Neural Network Combined with a Bioelectric Cellular Biosensor

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