Table 1.
Applications of e-nose for monitoring adulteration in food
| Category | Product | Adulteration with | Type of e-nose configuration for adulteration monitoring | Detection result to recommend best technique | References |
|---|---|---|---|---|---|
| Edible oils | Camellia seed oil and sesame oil | Maize oil | Commercial type, Airsense PEN2 (Germany) e-nose system with 10 MOS sensors | LDA: 83.6% for camellia seed oil while 94.5% for sesame oil | Hai and Wang (2006a) |
| sesame oil | Maize oil | Commercial type, PEN2 (Airsense corporation, Germany) e-nose with 10 MOS sensors | LDA: only one sample is incorrectly classified out of one hundred sixty five samples | Hai and Wang (2006b) | |
| Virgin olive oil | Sunflower oil and olive–pomace oil | Commercial type, two array chambers of Alpha MOS e-nose (FOX 3000) with 12 (6 + 6) MOS based sensors | LDA: classification accuracy > 96% and in some cases almost 100% | Cerrato Oliveros et al. (2002) | |
| Olive oil | Rapseed oil and sunflower oil | Commercial type, Fox 4000 Alpha MOS e-nose (Toulouse, France) with 3 sensor chamber each containing 6 gas sensors | PLS: correlation coefficient of 0.989 for rapseed adulteration of olive oil while, 0.990 for sunflower adulteration of olive oil | Mildner-Szkudlarz and Jeleń 2010 | |
| Olive oil | Hazelnut oil | Commercial type, Alpha MOS e-nose (Fox 4000) system with 3 chambers each containing six MOS sensors |
PCA: 96% separation of samples PLS: correlation coefficient 0.997 |
Mildner-Szkudlarz and Jeleń (2008) | |
| Argan oil | Sunflower oil | Experimental type, e-nose system with 5 MOS gas sensors | PCA: 98.81% in comestible argan oil adulteration and 98.09% in cosmetic argan oil adulteration | Bougrini et al. (2014) | |
| Soybean oil | Old frying oil | Experimental type, 8 MOS gas sensor based e-nose system | PLS: correlation coefficient of 0.843 for old frying oil adulteration of soybean oil | Men et al. (2014) | |
| Virgin coconut oil | Palm kernel olein oil | Commercial type, zNose™ (Model 7100 Electronic Sensor Technology, Newbury Park, CA, USA) e-nose with SAW sensor | PLS: correlation coefficient of 0.91 for palm olein oil adulteration of virgin coconut oil | Marina et al. (2010) | |
| Palm olein oil | palm stearin oil | Commercial type, MS-E-nose (SMart Nose300; SMart Nose, MarinEpagnier, Switzerland) | DFA: coefficient of determination for DF1 is 0.997 and DF2 is 0.966. DFA could able to discriminate below 10% adulteration level | Hong et al. (2011) | |
| Palm olein | Lard | Commercial type, zNose™ (Newbury Park, USA) based on SAW detector | ANOVA: coefficient of determination is 0.906 and Pearson’s correlation coefficient > 0.90 | Man et al. (2005) | |
| Peony seed oil | Soybean oil, corn oil, sunflower oil, rapeseed oil | Commercial type, PEN3 e-nose (Airsense analytics, Germany) with 10 MOS chemical gas sensors | LDA: soybean oil and sunflower oil can be successfully discriminated from peony seed oil | Wei et al. (2018) | |
| Milk and dairy products | Milk | Water and reconstituted milk powder | Commercial type, PEN2 e-nose (WMA Airsense Analysentechnik, Germany) with 10 MOS sensors | LDA: adulterated samples can be discriminated from milk stored for 1–4 days | Yu et al. (2007) |
| Milk | Aqueous CH2O, H2O2, NaClO | Experimental type, e-nose device based on 10 MOS gas sensors | SVM: 94.64%, 92.85%, 87.75% for aqueous CH2O, H2O2, NaClO adulteration in milk, respectively | Tohidi et al. (2018a) | |
| Milk | Detergent powder | Experimental type, e-nose system with 8 MOS sensors | SVM: 92.42% classification accuracy | Tohidi et al. (2018b) | |
| Cheese | Oscypek-like cheeses produced from ewe’s milk and cow’s milk | Commercial type, SPME-MS based e-nose | SVM: 97.9% classification accuracy | Majcher et al. (2015) | |
| Ghee | Sunflower oil and cow body fat | Experimental type, e-nose system with 8 different MOS gas sensors | PCA: 96% and 97% accuracy for sunflower oil and cow body fat adulteration of ghee, respectively | Ayari et al. (2018a) | |
| Ghee | Margarine | Experimental type, e-nose system with 8 different MOS gas sensors | PCA: 98% classification accuracy | Ayari et al. (2018b) | |
| Honey | Honey | Corn syrup and rice syrup | Commercial type, GC-Heracles e-nose (Alpha MOS, Toulouse, France) | PCA and PLS: Both the classifiers couldn’t well classified the syrup adulterations in honey | Gan et al. (2016) |
| Honey | Beetroot sugar and cane sugar | Commercial type, Cyranose 320 (Pasadena, CA, USA) e-nose system with 32 polymer sensors | LDA: 76.5% and 74.9% accuracy for stepwise LDA and direct LDA, respectively | Subari et al. (2012) | |
| Honey | Beetroot sugar and cane sugar | Commercial type, Cyranose 320 (Pasadena, CA, USA) e-nose system with 32 polymer sensors | ANN: mean absolute error of 6.9% for fusion data while 15% for e-nose separately | Subari et al. (2014) | |
| Honey | 2 different brands of sugar syrup | Commercial type, Cyranose 320 e-nose system with 32 polymer matrix sensors blended with carbon black | PNN: 92.59% classification accuracy | Zakaria et al. (2011) | |
| Alcoholic and non-alcoholic drinks | Liquor | Different geographical spirits | Commercial type, Alpha MOS based SA-Flash GC e-nose (HERACLES II, France) | DFA: 100% classification accuracy | Peng et al. (2015) |
| Italian wine | Ethanol, methanol, and other brands of wine | Experimental type, e-nose based on 4 MOS for headspace analysis | ANN: 93% of correct classification | Penza and Cassano (2004) | |
| Fruit juice | Alcohol | Commercial type, Cyranose 320 e-nose system with 32 carbon/polymer matrix sensors |
ANN, SVM: apple juice–Raki mixture, 98.33% LDA: lemon juice–alcohol mixture, 95% ANN: orange juice–vodka mixture, 96.67% KNN: sour cherry juice–alcohol mixture, 91.67% |
Ordukaya and Karlik (2016) | |
| Cherry tomato juices | Overripe tomato juices | Commercial type, PEN2 e-nose (Airsense Analytics, Schwerin, Germany) chamber with 10 MOS sensors | PCR: R2 > 0.99 for both training and prediction set of data | Hong et al. (2014) | |
| Fresh cherry tomato juice | Overripe tomato juices upto 30 per cent | Commercial type, PEN2 e-nose (Airsense Analytics, Schwerin, Germany) with 10 MOS positioned in a small chamber | SVM: 100% classification accuracy | Hong and Wang (2014) | |
| Freshly squeezed orange juice | 100% concentrated orange juice | Commercial type, FOX 3000 e-nose (Alpha MOS, Toulouse, France) equipped with 12 MOS sensors | LDA: 91.7% classification accuracy | Shen et al. 2016 | |
| Meat | Minced mutton | Pork | Commercial type, PEN2 e-nose (Airsense Corporation, Germany) with 10 different MOS sensors positioned in a small chamber | BPNN: correlation coefficient > 0.97 for both calibration and validation | Tian et al. (2019) |
| Mutton | Duck meat | Commercial type, PEN3 e-nose (Airsense Corporation, Germany) with 10 MOS sensors in sensor array | LDA: 98.2% classification accuracy | Wang et al. (2019) | |
| Lard | Chicken fat | Commercial type, zNoseTM e-nose (7100 Vapour Analysis System, Electronic Sensor Technology, Newbury Park, CA) with SAW sensor | PCA: 90% classification accuracy | Nurjuliana et al. (2011) | |
| Spices | Spice mixtures | Curry and garlic | Commercial type, e-nose (KAMINA-type, Yson GmbH) contains a chip array of 38 sensor segments based on gas sensitive doped tin oxide | PCA: 93.8% classification accuracy | Banach et al. (2012) |
| Saffron | Aroma fingerprints of saffron with yellow styles, safflower and dyed corn stigma | Experimental type, e-nose system with 6 MOS sensors (HANWEI Electronics Co., Ltd., Henan, China) | ANN: 86.87% and 100% accuracy for saffron adulteration with yellow styles-dyed corn stigma and safflower, respectively | Heidarbeigi et al. (2015) | |
| Cumin | Coriander | Experimental type, e-nose system with 6 MOS sensors | PCA: 91.68% classification accuracy | Tahri et al. (2017) | |
| Saffron | Artificially colored safflower and artificially colored yellow styles of saffron | Experimental type, e-nose system with 7 MOS sensors |
ANN: correlation coefficient of 0.95 and 0.97 for color and aroma based adulteration, respectively SVM: 100% success rate only on aroma datasets |
Kiani et al. (2017) |