Food Analysis and Its Methods in Practice Report

Introduction

The issue of food quality creates numerous concerns, especially about the composition, safety, and nutritional value of food (Huang, Liu & Ngadi 2014). Food analysis is the field that handles the use of diagnostic processes to characterize food substances and their components (Nielsen 2017). Analytical procedures provide information regarding different properties of foods such as their structure, constituents, physical, and chemical attributes as well as sensory features. These facts facilitate the comprehension of factors that influence food properties in addition to increasing the capacity to produce safe, nutritious, and affordable food. Food analysis is conducted by scientists in various spheres of the food industry, for example, food producers, ingredient dealers, diagnostic laboratories, government bodies, and research laboratories in institutions of higher learning.

Government policies are necessary to ensure the continuous supply of healthy, safe, and nutritious food to its citizens. These rules also inform consumers concerning the nutritional constitution of foods to help them make informed decisions about their diets, encourage fair competition among food manufacturers, and get rid of economic deception (Babu, Gajanan & Sanyal 2014). Government agencies that monitor food quality include the United States Department of Agriculture (USDA), the Food and Drug Administration (FDA), and the Environmental Protection Agency (EPA) among others.

Different methods of food analysis exist depending on the type of food compound being investigated. For this experiment, five methods will be used to analyze the food sample. They include Ankom extraction, Dumas nitrogen, oven drying at 100 ^oC, furnace combustion at 550 ^oC and high-performance liquid chromatography (HPLC). Ankom extraction is an analytical technique aimed at finding the fat content of food by removing crude (free) fat using solvents such as petroleum ether. The crude fats that are extracted consist of triglycerides and other interrelated lipids. The loss of mass attributed to the removal of fat or oil from the sample is then quantified. The Ankom method has been used successfully in the extraction of oil from sunflower (Scharlack, Aracava & Rodrigues 2017), pistachio seeds (Rabadán et al. 2017), and corn germ (Navarro et al. 2016).

Dumas extraction, on the other hand, is a mechanized instrumental method with the capacity to measure the total nitrogen content of food. Dumas extraction is a fast and safe substitute for the Kjeldahl standard technique (Chang & Zhang 2017). The nitrogen content of food indicates its protein content since nitrogen makes up the amine group in amino acids. The Dumas method entails three steps: combustion, reduction and separation, and detection. During combustion, the sample is weighed and rid of any atmospheric gases before being heated at elevated temperatures (1,030 ºC) in the presence of pure oxygen. The combustion process yields carbon dioxide, nitrogen dioxide, water, and nitrogen in the form of different oxides (NyOx). In the reduction and separation phase, oxygen is removed from the incineration products to transform the oxides of nitrogen into molecular nitrogen, whose signal is detected and measured.

Moisture content determination by oven drying entails measuring the weight loss that results from evaporation of water at 100 ^oC. The main shortcoming of this technique is that the final figure may not be an accurate representation of the actual water content because other volatile substances such as oils may be lost at this temperature (Nielsen 2017). Conversely, foods like cereals have small amounts of free water that is lost during the normal drying process. The remaining water is associated with sugars and proteins and cannot be removed during analysis. Additionally, the amount of free water removed is proportional to the temperature used. Consequently, only samples analyzed at similar temperatures should be compared. The ashing method determines the inorganic content of food, which is represented by the mineral content. It is presumed that combustion at high temperatures eliminates organic matter and leaves the inorganic constituents. However, there may be additional losses through volatilization.

HPLC identifies and measures small organic substances in a liquid mixture. Purified samples are introduced into an HPLC column and eluted using a solvent (Nielsen 2017). Constituents of the sample are separated on the column according to their sizes as well as attraction to the molecules in the column or solvent. The temperature, type of solvent used, and flow rate also determine the magnitude of separation. The main shortcoming of HPLC is that it is very costly. The purpose of this experiment was to conduct a food analysis of an unknown sample and identify it.

Method

A 100-gram sample of an unknown ground food material (sample ID 537) was provided in an airtight jar. The sample was analyzed for moisture content by oven drying at 100 ^oC and ash content by furnace combustion at 550 ^oC as described in the laboratory manual. The findings of the tests were summarized in Table 1. The data were also used to develop a nutritional information panel.

Results

Ash Calculation

Ash g/100g = (Crucible & ashed sample – Crucible wt) x 100

(Crucible & wet sample – Crucible wt) 1

For replicate 1: Ash g/100 g = (31.0606 – 31.0171) x 100

(33.5954 – 31.0171) 1

= (0.0435/2.5783) ×100

=1.69 g/100 g

For replicate 2: Ash g/100 g = (31.9673- 31.9376) x 100

(34.6584- 31.9376) 1

= (0.0297/2.7208)×100

= 1.09 g/100 g

For replicate 3: Ash g/100 g = (31.08 – 31.0342) x 100

(33.6333 – 31.0342) 1

= (0.0458/2.5991)×100

=1.76 g/100 g

The average Ash g/100 g for the three samples = (1.69+ 1.09+1.76) /3

= 1.51 g/100 g

Total Solids Calculation

Total solids g/100 g = (Pan & dry sample – Pan weight) x 100

(Pan & wet sample – Pan weight) 1

Moisture g/100 g = 100 – Total Solids g/100 g

For replicate 1: Total solids g/100 g = (45.7471 – 41.6773) x 100

(45.8918 – 41.6773) 1

= (4.0698/4.2145) x 100

= 96.57 g/100 g

Moisture g/100 g = 100 – 96.57 g/100 g

= 3.43 g/100 g

For replicate 2: Total solids g/100 g = (41.3839 – 37.6209) x 100

(41.5189 – 37.6209) 1

= (3.763/3.898) x 100

= 96.54 g/100 g

Moisture g/100 g = 100 – 96.54 g/100 g

= 3.46 g/100 g

For replicate 3: Total solids g/100 g = (47.2309 – 43.0353) x 100

(47.3805 – 43.0353) 1

= (4.1956/4.3452) x 100

= 96.56 g/100 g

Moisture g/100 g = 100 – 96.56 g/100 g

= 3.44 g/100 g

Average moisture = (3.43+3.43+3.44) /3

= 3.45 g/100 g

Table 1. Summary of assay results.

Table 2. Nutrition information panel for sample 537.

Discussion

The moisture content of the sample was found to be 3.45 g/100 g, whereas the ash content was determined as 1.51 g/100 g. These values closely resembled sample 4, which had an ash content of 1.5 g/100 g and a moisture content of 3.7 g/100 g. However, there were slight differences between the unknown sample and the reference sample due to inaccuracies in the methods used to analyze the two samples.

Similar conditions should be maintained when determining the moisture content of food to guarantee the reproducibility of the findings, which is usually challenging in real life settings. The fraction of free water that is lost is directly proportional to the temperature used. Therefore, only findings obtained using the same drying method should be compared. The moisture content of the reference sample was obtained at 100 ^oC just like in this experiment. However, minor disparities in the findings were still observed. Possible sources of error in the analysis of the moisture content included the precision and resolution of the weighing balance as well as the heating temperature and duration. Additionally, the samples probably absorbed moisture from humid surroundings or lost moisture due to excessive drying or baking (Pomeranz 2013). Therefore, moisture results alone cannot be used to ascertain the identity of food samples. Instead, the dry weight composition is often useful in minimizing some of the disparities associated with moisture content determination.

The amount of ash is considered an overall measure of food quality, hence, it is used to identify food. Determining the ash content of food often entails burning the organic matter in food leaving behind inorganic minerals. This method is based on the assumption that heating does not destroy minerals and that minerals are relatively stable compared to other nutrients. Determination of the ash and mineral content of foods is important for nutritional labeling, quality determination, microbiological stability, and processing (Nielsen 2017). In addition, the mineral content of food influences physiochemical attributes of foods and holds back the growth of microorganisms (Liu et al. 2015). The ash content value was not an exact match with the reference sample due to possibilities of errors in the analysis.

Dry ashing, which entails heating the food sample to high temperatures between 500 and 600 ^oC, was used in this experiment. The major shortcoming of this method, which could have contributed to the discrepancies in the findings, is that volatile minerals such as mercury, lead, and iron may be lost (Sezer et al. 2017). Another source of errors concerns the sample preparation method. Solid food samples should be ground to a fine consistency and mixed carefully. Moreover, high-moisture samples should be dried, whereas high-fat samples should be defatted before ashing. Therefore, the differences in the findings could also be linked to discrepancies in the sample preparation techniques. Other problems included possible adulteration of samples by minerals in mills, glassware or crucibles during the analysis.

Studies show that the ash matter of fresh foods seldom goes beyond 5% (Nielsen 2017). However, certain processed foods such as dried beef may have high ash contents in the range of 12%. This observation implied that the unknown sample could be fresh food. The nutrition information panel (Table 2) indicated that each serving of sample 537 provided approximately 0.8625 grams of water and 0.3775 grams of ash. The protein, fat, and sugar content of the food sample was not determined. Therefore, it was impossible to determine the carbohydrate and energy calculations.

Conclusion

The unknown food sample had a close match to the known sample 4. This identity was confirmed by comparing the moisture and ash contents of the unknown and the reference samples in Table 1 of the laboratory manual. However, the values of the unknown and reference samples were not exact matches despite using similar methods of analysis. These disparities could be attributed to errors during sample preparation and the real analysis.

References

Babu, S, Gajanan, SN & Sanyal, P 2014, Food security, poverty and nutrition policy analysis: statistical methods and applications, 2nd ed, Academic Press, San Diego, CA.

Chang SKC, & Zhang Y 2017, ‘Protein Analysis’, in SS Nielsen (ed.), Food analysis, 5th edn, Springer, New York, NY, pp. 315-331.

Huang, H, Liu, L & Ngadi, MO 2014 ‘Recent developments in hyperspectral imaging for assessment of food quality and safety’, Sensors, vol. 14, no. 4, pp. 7248-7276.

Liu, J, Pan, Y, Yao, C, Wang, H, Cao, X & Xue, S 2015, ‘Determination of ash content and concomitant acquisition of cell compositions in microalgae via thermogravimetric (TG) analysis’, Algal Research, vol. 12, pp. 149-155.

Navarro, SL, Capellini, MC, Aracava, KK & Rodrigues, CE 2016, ‘Corn germ-bran oils extracted with alcoholic solvents: extraction yield, oil composition and evaluation of protein solubility of defatted meal’, Food and Bioproducts Processing, vol. 100 Part A, pp. 185-194.

Nielsen, SS 2017, Food analysis laboratory manual, Springer, New York, NY.

Pomeranz, Y (ed.) 2013, Food analysis: theory and practice, Springer Science & Business Media, New York, NY.

Rabadán, A, Álvarez-Ortí, M, Gómez, R, Alvarruiz, A & Pardo, JE 2017, ‘Optimization of pistachio oil extraction regarding processing parameters of screw and hydraulic presses’, LWT-Food Science and Technology, vol. 83, pp. 79-85.

Scharlack, NK, Aracava, KK & Rodrigues, CE 2017, ‘Effect of the type and level of hydration of alcoholic solvents on the simultaneous extraction of oil and chlorogenic acids from sunflower seed press cake’, Journal of the Science of Food and Agriculture, vol. 97, no. 13, pp. 4612-4620.

Sezer, B, Bilge, G, Sanal, T, Koksel, H & Boyaci, IH 2017, ‘A novel method for ash analysis in wheat milling fractions by using laser-induced breakdown spectroscopy’, Journal of Cereal Science, vol. 78, pp. 33-38.