Unstable milk occurrence in the semi-arid region and its relation with the physico-chemical characteristics of milk

Priscilla Fernandes Faria, Adriano Henrique do Nascimento Rangel, Stela Antas Urbano, Luis Henrique Fernandes Borba, José Geraldo Bezerra Galvão Júnior and Edine Roberta da Silva

Universidade Federal do Rio Grande do Norte, Campus Universitário, Lagoa Nova, Natal, Rio Grande do Norte, Brazil, Postal code 59078-970.
stela_antas@yahoo.com.br

Abstracts

In the Northeast region of Brazil, there are few studies that indicate occurrence and quality of unstable non-acid milk (UNAM). This study aimed to identify the occurrence of UNAM and to determine its physicochemical characteristics in the semi-arid region of Rio Grande do Norte. 176 raw milk samples were analyzed, originating from 23 cooling tanks located in 07 municipalities. The samples were collected in duplicate; one was used for physical testing (alcohol, acidity, pH, electrical conductivity and boiling tests) and the other for determining milk composition (fat, protein, lactose, total solids, casein and ureic nitrogen levels) and somatic cell count (SCC).

The alcohol test failed 31.82% of the samples, of which 50% were non-acid, and 50% had high acidity. The occurrence of UNAM was 15.91%. The samples were divided into 3 classes: stable milk, UNAM and acid milk. There was no significant difference between the compositions and SCC of stable (normal) and UNAM milk (p>0.05). The average value for electric conductivity was 4.86±0.38 mS/cm for stable milk, 4.55±0.26 mS/cm for unstable acid milk and 4.53 mS/cm for UNAM. The boiling test was negative for all UNAM samples. UNAM showed to have quality similar to stable milk adequate thermal stability, proving that there is no reason for industries to dispose of this milk.

Key words: alcoholic instability, casein stability, Dornic acidity, electric conductivity

Introduction

The demand for quality is an inherent feature of the 21^st century consumer, who remains alert to the influence that consumed foods have on their health and quality of life. Therefore, they seek out and prefer to consume safe food, especially when it comes to those of animal origin. In this sense, several tests are used to check the quality of milk that leaves production units destined for milk processing plants. The alcohol test is the method routinely used to verify the stability of raw milk proteins through dehydration caused by alcohol in different concentrations, and is used to estimate the stability of milk when subjected to heat treatment (Marques et al 2007).

Unstable non-acid milk (UNAM) is defined as milk that has lost casein stability in the alcohol test, with no change in acidity. This occurrence causes significant damage to the entire production chain, as the milk is then rejected or undervalued by the industry since it is not considered fit for processing, leading to a low yield when used in dairy (Roma Jr. et al 2009).

However, it has been reported in literature (Maluf and Ribeiro 2012) that stability to alcohol is not directly related to stability in thermal processing; thus, disposing of UNAM would be unnecessary in some cases. Moreover, regarding the composition, different results have been obtained when evaluating physical, chemical and microbiological UNAM quality parameters (Marques et al 2007; Zanela et al 2009; Oliveira et al 2011).

The high incidence of UNAM described in the South and Southeast regions (Marx et al 2011) raises the concern of evaluating the quality of this milk and its occurrence in other regions. Therefore, this study aimed to identify the occurrence of UNAM in the semi-arid region of Northeastern Brazil and to evaluate its physicochemical characteristics.

Material and Methods

Samples from 23 cooling tanks distributed among the Central and West meso-regions of Rio Grande do Norte were evaluated from September to December 2014. Both regions present a semi-arid climate according to the Köppen classification, with low rainfall concentrated in the months of January to April (Santos et al 2012). All evaluated milk samples were collected and processed by the APASA (Associação dos Pequenos Agropecuaristas do Sertão de Angicos) processing plant, located in the city of Angicos, RN.

Samples were collected monthly, directly from cooling tanks, and always after milk homogenization by mechanical stirring, comprising 176 samples. Each sample was collected in duplicate in properly identified 40mL plastic vials, and maintained at a temperature between 4 and 6°C. A sample containing Bronopol® (2-bromo-2-nitro-1,3-propanediol) was sent to the APCBRH laboratory (Associação Paranaense de Criadores de Bovinos da Raça Holandesa) in Curitiba-PR, for determining somatic cell counts and milk composition analysis. Determination of fat, protein, lactose, total solids, casein and ureic nitrogen levels were performed using the Fourier transform infrared spectroscopy method and somatic cell count by flow cytometry method, both carried out using Bentley Nexgen^® equipment (Bentley Instruments, Chasca MN, USA).  The equipment remained properly calibrated using standard samples sent bimonthly from VALACTA (Dairy Production Centre of Expertise Quebec - Atlantic Canada), which were prepared through chemical method, where casein is precipitated to pH 4.6 and precipitate was analyzed by the Kjeldahl method. The other sample was stored in a bottle without any additives and was sent to the LABOLEITE laboratory (UFRN), where alcohol, acidity, pH, electrical conductivity and boiling tests were performed. pH and electric conductivity were obtained from a Model pH-1500 portable digital equipment (Instrutherm®).

The alcohol test was carried out in Petri dishes with the addition of 2ml of milk and 2ml of ethanol solution at 68, 72 and 76% (v/v) concentrations, followed by homogenization for 10 seconds. The results were analyzed soon after. Positive samples for alcohol test, in any degree, and with acidity and between 14 and 18°D were considered as unstable non-acid milk. Negative sampling for the alcohol test in all three concentrations was considered as normal milk. Finally, positive sampling for the alcohol test in any graduation showing acidity above 18°D was considered as acid milk.

All samples were submitted to titratable acidity test by titration method using sodium hydroxide (NaOH) solution 0.1 N.

For the boiling test, 10ml of milk samples were placed in a beaker and put on an electric heater plate until boiling. Samples showing milk coagulation after cooling were considered positive.

The obtained data were grouped for statistical analysis on types of milk (normal and UNAM), alcoholic degree for unstable milk (72° and 76°) and milk classes (stable milk, unstable non-acid milk and unstable acid milk). The following mathematical models were used:

a)  Considering the type of milk

Where:

Type_i = effect of i-^th type of milk composition  considering normal or UNAM milk;

b)  Considering the alcohol content of unstable milk:

Where:

Degree_i = effect of i-^th alcoholic degree of unstable milk on the composition variables, acidity and somatic cell count, considering unstable milk with alcohol content of 72% and 76%;

c)    Finally, considering the milk class:

Class_i = effect of i-^th milk class on the composition variables of acidity and somatic cell count for the milk classes: stable, unstable non-acid milk, unstable acid milk.

For all models:

Y_ij = dependent variables of composition, acidity and somatic cell count;

= overall average of the dependent variables of the model;

= residual effect.

Data were arranged in a completely randomized design with three types of milk (UNAM, acid milk and stable milk) and analyzed month to month. Statistical analyzes of the data were performed according to the mathematical models described above through the PROC GLM using SAS version 9.0 (SAS Institute, 2002). A comparison between means was made by Tukey test at 5% significance. Next, variance analysis was performed to check the influence of the independent variables (types of milk - normal and UNAM; alcoholic degree/content for unstable milk - 72° and 76°) and milk classes (stable milk, unstable non-acid milk and unstable acid milk).

Results and Discussion

A total of 176 raw milk samples were analyzed, where 34.10% showed abnormality in the acidity parameter, with 32.95% acid samples (>18°D) and 1.15% alkaline (<14°D). Thus, 65.90% of the samples complied with the acidity parameters required by the Ministry of Agriculture (MAPA 2011). These results were better than those obtained by Marx et al (2011), who obtained 68.11% of samples with non-acceptable acidity parameters.

In the alcohol test, 31.82% of the samples reacted positively. However, only 50% of these were classified as unstable non-acid milk (UNAM), with acidity between 14-18°D. Thus, the occurrence of UNAM was 15.91% of the total samples. Considering the results, it is observed that 50% of this milk could be mistakenly classified as acidic, since the processing platforms mistakenly use the alcohol test to measure acidity, which would result in economic losses to both the producer and the industry due to rejection or undervaluation of the product (Roma Jr. et al 2009). Thus, there is the need for new methodologies to be indicated by the relevant supervisory bodies, avoiding misinterpretation and improper disposal of produced milk.

The percentage of UNAM observed in this study was lower than those found by Marques et al (2007) (44.1%); Zanela et al (2009) (55.2%); Oliveira et al (2011) (64.8%); Souza et al (2011) (33.7%); Marx et al (2011) (33%); Fagnani et al (2014) (30.2%) and Sovinski et al (2014) (19.4%). It is emphasized that under the rules of the Ministry of Agriculture (MAPA 2011), the alcohol test should be performed at a concentration of 72% (v/v). However, several milk receiving industries perform such testing at higher concentrations (Marques et al 2007; Fischer et al 2012).

Milk composition, somatic cell count and acidity did not differ between normal milk and UNAM (p>0.05) (Table 1).

The attributes evaluated in milk are within specified limits according to Standard Regulation No. 62 (MAPA 2011). Similar results were found by Marx et al (2011) and Fagnani et al (2014).

UNAM presented acceptable values of acidity and SCC, making it safe in microbiological terms. Regarding values obtained for fat, Marques et al (2007) and Oliveira et al (2011) found significantly higher values for fat in the UNAM when compared to stable milk. As fat content varies depending on many factors - such as diet, time of year and lactation stage - it is possible that the changes found by these authors cannot be directly related to the occurrence of UNAM.

The values for protein content were higher than those found by Oliveira and Timm (2006), but there was no significant difference for either. Zanela et al (2006) and Fischer et al (2006) observed increased ureic nitrogen in UNAM compared to normal milk, however, no similar effect was found in this study. Sweetsur and Muir (1981) indicated that ureic nitrogen is the milk component that most interferes with stability, showing a positive correlation between ureic nitrogen content and alcoholic stability.

It is worth mentioning that increased milk urea causes a decrease in yield in cheese manufacturing, and increases the clotting time, as the true protein that is responsible for the formation of mass is replaced by ureic nitrogen (Ferreira et al 2006).

Botaro et al (2009) and Oliveira et al (2011) indicated that milk composition is more subject to changes depending on the time of year, production, breed and diet than to the occurrence of UNAM. As a result, changes in milk composition would be the triggering cause for UNAM (Chavez et al 2004), and not a consequence, as was initially suggested. This would explain the low incidence of UNAM, since there was no significant difference for any constituent, including casein.

Regarding Dornic acidity, Chavez et al (2004), Marx et al (2011) and Oliveira et al (2011), found similar values to those obtained for acidity in stable samples and UNAM.

It is also worth noting that no UNAM samples presented coagulation for the boiling test, similar to the results found by Maluf and Ribeiro (2012). This result makes UNAM fit for consumption and processing, since there was no loss of nutritional quality nor thermal stability.

For total unstable non-acid samples, no samples were positive at the minimum alcohol concentration (68%), 21.4% showed instability at the concentration of 72%, and 78.6% showed instability at 76%, thus demonstrating that the higher the alcohol concentration, the greater the instability of the casein. Zanela et al (2006) found a higher frequency of positive results in the 76% alcohol test, as well as Marques et al (2011), who reported a negative relationship between the stability of milk and the alcohol content.

In comparing the positive UNAM to 72% and 76% alcohol, there was no significant difference (p>0.05) between milk constituents, indicating that increased alcohol content by industries does not guarantee milk with higher quality or thermal stability (Table 2).

Chaves et al (2004) cited casein reduction as a triggering factor for UNAM, however, this effect was not observed in this study.

The physico-chemical characteristics of unstable samples at 76% were within the Parameters required by Normative Instruction 62 (MAPA 2011). There was no clotting in the boiling test, confirming that increased alcohol content by industries is not necessary since it does not guarantee better nutritional quality or better thermal stability, and causes losses to producers who have their milk disposed of unnecessarily.

Despite the experiment being conducted in the dry season which is considered as the highest incidence of UNAM (Botaro et al 2009; Oliveira et al 2011; Fischer et al 2012), the number of positive samples was low, indicating that more studies should be conducted since the study area has three triggers for UNAM: heat stress, unbalanced diet and intense dry periods. According to Zanela et al (2009), the percentage of UNAM may vary depending on the month and according to region, which may explain the inferior results obtained in this study.

The analyzed samples were divided into three classes (stable milk, unstable non-acid milk and acid milk) to determine the average electrical conductivity for each class (Table 3).

There was a significant difference (p<0.05) for electrical conductivity in relation to stable milk and unstable milk (acid and non-acid). The electrical conductivity was higher in stable milk (4.84 mS/cm) than in unstable acid milk (4.55 mS/cm) or non-acid milk (4.53 mS/cm). This can be explained by the higher concentration of Ca++ ions in unstable milk (Omoarukhe et al 2010; Marques et al 2011), which contributes to the reduction of milk electrical conductivity (Nielen et al 1992).

Acidity (ºD) and pH were analyzed in samples separated according to MEC classes: less than 5 mS/cm; greater than or equal to 5 mS/cm. Higher values were observed (p<0.05) for acidity and pH when the electric conductivity was below 5 mS/cm (Table 4). Della Libera et al (2011) found different results, indicating that highest MEC class showed higher pH, while Fraga et al (2009) found no correlation between MEC and pH, displaying inconsistent results still related to the occurrence of UNAM.

Conclusions

• The incidence of UNAM in the region and the study period was considered low. Its occurrence did not affect the milk composition or somatic cell count. However, electrical conductivity presented significant variation between normal milk and unstable acid and non-acid milk

  
  

• Despite the low incidence, this result shows that there may be mistakes in disposing milk at the receiving platform by retail establishments, taking into account the alcohol test to detect the acidity of milk. In this scenario, the disposal of UNAM only based on the alcohol test causes great loss, affecting milk producing and processing establishments.

References

Botaro B G, Lima Y V R, Cortinhas C S, Rennó F P and Santos M V D 2009. Effect of the kappa-casein gene polymorphism, breed and seasonality on physicochemical characteristics, composition and stability of bovine milk. Revista Brasileira de Zootecnia 38(12), 2447‑2454. Retrieved January 12, 2016, from http://www.scielo.br/scielo.php?pid=S1516-35982009001200022&script=sci_arttext

MAPA Ministério da Agricultura, Pecuária e Abastecimento 2011. Instrução Normativa n.062 de 29 de dezembro 2011. Aprova os Regulamentos técnicos de produção, identidade e qualidade do leite tipo A, do leite tipo B, do leite tipo C, do leite pasteurizado e do leite cru refrigerado e o regulamento técnico da coleta do leite cru refrigerado e seu transporte a granel. Brasília, 2011. Retrieved August 23, 2014, form http://www. agricultura.gov.br/Instrução Normativa 62

Chavez M S, Negri L M, Taverna M A and Cuatrín A 2004. Bovine milk composition parameters affecting the ethanol stability. Journal Dairy Research 71(2), 201-206. Retrieved August 10, 2016, from http://journals.cambridge.org/download.php?file=%2FDAR%2FDAR71_02%2FS0022029904000172a.pdf&code=ee99896863afcc119ea5e022a6df2e32

Della Libera A M M P, Souza F N, Blagitz M G and Batista C F 2011. Avaliação de indicadores inflamatórios no diagnóstico da mastite bovina. Arquivos do Instituto Biológico 78(2), 297-300. Retrieved March 16, 2016, from http://www.revistas.bvs-vet.org.br/arqib/article/download/25329/26198

Fagnani R, Beloti V and Battaglini A P P 2014. Acid-base balance of dairy cows and its relationship with alcoholic stability and mineral composition of milk. Pesquisa Veterinária Brasileira 34(5), 398-402. Retrieved February 02, 2016, from http://www.scielo.br/scielo.php?pid=S0100-736X2014000500002&script=sci_arttext

Ferreira M G, Souza L T, Peleja L and Corassin C H 2006. Uréia e qualidade do leite. Revista Científica Eletrônica de Medicina Veterinária 3(6), 7-10. Retrieved February 02, 2016, fromhttp://faef.revista.inf.br/imagens_arquivos/arquivos_destaque/bJjJkw1Dvs2wlaS_2013-5-20-15-27-44.pdf

Fischer V, Marques L T, Zanela M B, Fruscalso V, Ribeiro M E R, Stumpf Júnior W and Barbosa R S 2006. Chemical composition of unstable non-acid milk. In International Workshop on the Biology of Lactation Farm Animals. Revista de Ciências Veterinárias 4(4 s.1), 52.

Fischer V, Ribeiro M E R, Zanela M B, Marques L T, Abreu A S D, Machado S C, Fruscalso V, Barbosa R S and Stumpf M T 2012. Leite instável não ácido: um problema solucionável? Revista Brasileira de Saúde e Produção Animal 13(3), 838-849. Retrieved February 19, 2016, from http://revistas.ufba.br/index.php/rbspa/article/view/2600/1347

Fraga R, Raimondo S, Brandespim F B, Prina A P M and Junior E H B 2009. Avaliação do pH e da eletrocondutividade do leite de bovinos da raça Jersey durante o primeiro mês de lactação. Semina: Ciências Agrárias 30(2), 447-456. Retrieved January 29, 2016, from http://www.uel.br/revistas/uel/index.php/semagrarias/article/view/2603/2258

Maluf R S and Ribeiro A B 2012. Resíduos de Antibióticos em Leite. SaBios - Revista Saúde e Biologia 7(1), 30-44. Retrieved March 01, 2016, from http://revista.grupointegrado.br/revista/index.php/sabios2/article/viewArticle/923

Marques L T, Fischer V, Zanella M B, Ribeiro M E R, Stumpf Junior W and Rodrigues C M 2011. Produção leiteira, composição do leite e perfil bioquímico sanguíneo de vacas lactantes sob suplementação com sal aniônico. Revista Brasileira de Zootecnia 40(5), 1088-1094. Retrieved February 17, 2016, from http://www.lume.ufrgs.br/bitstream/handle/10183/98428/000855764.pdf?sequence=1

Marx i g, Lazzarotto t c, Drunkler d a and Colla E 2011. Ocorrência de Leite Instável não Ácido na Região Oeste do Paraná. Revista Ciências Exatas e Naturais 13(1), 101-112. Retrieved March 14, 2016, from http://revistas.unicentro.br/index.php/RECEN/article/view/1256

Nielen M, Deluyker H, Schukken Y H and Brand A 1992. Electrical conductivity of milk: measurement, modifiers, and meta analysis of mastitis detection performance. Journal of Dairy Science 75(2), 606-614.

Oliveira D S and Timm C D 2006. Composição do leite com instabilidade da caseína. Ciência e Tecnologia dos Alimentos 26(2), 259-263. Retrieved March 14, 2016, from http://www.scielo.br/pdf/cta/v26n2/30170

Roma Júnior L C, Montoya J F, Martins T T, Cassoli L D and Machado P F 2009. Sazonalidade do teor de proteína e outros componentes do leite e sua relação com programa de pagamento por qualidade. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 61(6), 1411-1418. Retrieved March 17, 2016, from http://www.scielo.br/pdf/abmvz/v61n6/v61n6a22.pdf

Santos e Silva C M, Lúcio P S and Spyrides M H C 2012. Distribuição espacial da precipitação sobre o Rio Grande do Norte: estimativas via satélites e medidas por pluviômetros. Revista Brasileira de Meteorologia 27(3), 337-346. Retrieved January 27, 2016, from http://submission.scielo.br/index.php/rbmet/article/view/58747

Souza P P M, Soares K D A, Lima Filho C F, Mota R A, Silva M C D and Medeiros E S 2011. Ocorrência do leite instável não ácido em vacas leiteiras no município de Viçosa-AL. Veterinária Notícias 17(2), 144-147. Retrieved March 14, 2016, from http://www.seer.ufu.br/index.php/vetnot/article/view/19597

Sovinski  I, Cano F G, Raymundo N K L, Barcellos V C and Bersot L D S 2014. Situação da comercialização do leite cru informal e avaliação microbiológica e físico-química no município de Cafelândia, Paraná, Brasil. Arquivos de ciências veterinárias e zoologia da UNIPAR 17(3), 161-165. Retrieved March 14, 2016, from http://revistas.unipar.br/?journal=veterinaria&page=article&op=view&path[]=4938&path[]=2877

Sweetsur A W M and Muir D D 1981. Role of cyanate ions in the urea-induced stabilization of the caseinate complex in skim-milk. Journal of Dairy Research (48)1, 163-166.

Zanela M B, Fischer V, Ribeiro M E R, Barbosa R S, Marques L T, Stumpf Junior W and Zanela C 2006. Leite instável não-ácido e composição do leite de vacas Jersey sob restrição alimentar. Pesquisa Agropecuária Brasileira 41(5), 835-840. Retrieved March 23, 2016, from http://www.scielo.br/pdf/%0D/pab/v41n5/30605.pdf

Zanela M B, Ribeiro M E R, Fischer V, Gomes J F and Stumpf Junior W 2009. Ocorrência do leite instável não ácido no noroeste do Rio Grande do Sul. Arquivo brasileiro de medicina veterinária e zootecnia 61(4), 1009-1013. Retrieved February 15, 2016, from http://www.lume.ufrgs.br/bitstream/handle/10183/77056/000725193.pdf?sequence=1

Received 8 July 2016; Accepted 5 November 2016; Published 1 January 2017

Go to top