A Comparison of the Physical Properties Between Merino Wool and Camel Hair Through Discriminant Analysis

ABSTRACT

The applicability of camel hair is still conditioned by a gap in the knowledge about this raw material, which in turn hinders its supply chain, commercial possibilities, and the value that the product reaches in the market. The present research compares the physical-mechanical properties of Canarian camel hair versus Merino sheep wool, the world’s most popular proteinic fabric material, using a discriminant canonical analysis. The attributes that maximized differences between both types of animal-origin fibers were related to average fiber diameter, fineness, length staple, and residual dirt. Although slightly shorter than sheep wool, camel hair can reach a prominent fabric quality and spinning performance given its greater average diameter and its lower variation within small fragments along the snippet, as well as the higher distance from the tip to the finest point in the staple, which is an indicator for the breaking point. Such characteristics are known to increase bending rigidity during the manufacturing process. Residual dirt may be associated with the low-input, extensive exploitation of camels. The present paper provides a reference for maturing pre-process and manufacture techniques for the further valorization of camel hair in the present-day textile industry and, thus, engages income opportunities for this livestock production.

摘要

骆驼毛的适用性仍然受到对这种原材料知识差距的制约，这反过来又阻碍了其供应链、商业可能性和产品在市场上的价值. 本研究使用判别规范分析比较了加那利驼毛与世界上最流行的蛋白质织物材料美利奴羊毛的物理力学性能. 使两种动物来源纤维之间差异最大化的属性与平均纤维直径、细度、短纤维长度和残留污垢有关. 尽管驼毛比羊毛略短，但鉴于其较大的平均直径和沿片段的小片段内的较低变化，以及从尖端到缝合线中最细点的较高距离（这是断裂点的指标），驼毛可以达到显著的织物质量和纺纱性能. 已知这样的特性会在制造过程中增加弯曲刚度. 残留的泥土可能与骆驼的低投入、广泛开采有关. 本文为成熟的预加工和制造技术提供了参考，以进一步提高骆驼毛在当今纺织业中的价格，从而为这种畜牧生产创造收入机会.

KEYWORDS:

• Camel hair
• sheep wool
• optical fiber measurement
• comparative analysis
• niche market
• OFDA

KEYWORDS:

• 关键词
• 驼毛
• 羊毛
• 光纤测量
• 比较分析
• 利基市场

Introduction

Despite the fact that natural vegetable fibers (cotton, linen) and plastic-derived synthetic fibers (polyurethane, polyamide, and acrylic) have emerged within the textile and apparel manufacturing scene (Purvis and Robert Franklin 2005), sheep wool and other mammal hairs (i.e., goat, camel, or yak hair) remain among the most demanded natural fibers in the textile industry (Harizi et al. 2007). Particularly, Merino sheep wool is highly demanded and acknowledged as the finest and softest crimped fiber (Wang et al. 2014). However, other coarser but still smoother animal fibers, such as mohair, cashmere, camelid fleece, large bovid fiber, and Angora rabbit hair, are generally less appreciated for clothing fashion design but for the home textile market (Yam and Khomeiri 2015), despite the product diversification that the broad public often demands (Yondonsambuu, Altantsetseg, and Bator 2003).

In this context, Old-World camels’ hair fibers may constitute a huge, valuable textile resource due to their diverse nature (Burger, Ciani, and Faye 2019). Bactrian or two-humped camels (Camelus bactrianus) have been addressed as a source for quality thin fiber production in Asian local communities. The main niche that this specific fiber covers is the satisfaction of self-consumption and emerging European and North American exportation demands (Babu 2015; Zarrin et al. 2020). By contrast, dromedary or one-humped camel (Camelus dromedarius) fiber has been reported to attract consumers due to the comfortability this fiber confers to fabrics (Sharma and Pant 2013), which makes it especially suitable for the clothing industry. Still, this wide range of applications has not translated into increased attention paid by researchers in terms of production systems, selective breeding, best processing practices, and market opportunity fulfillment (McGregor 2018).

Furthermore, the exploration of hair production could lead to the consolidation of camel hair as a high-value functional niche for these species, which in turn may ensure the sustainability of endangered camel populations. Among these endangered populations, the Canarian camel is an autochthonous breed originally from Spain and unique in Europe (Iglesias Pastrana, Navas González, Ciani, Nogales Baena, et al. 2020), which may find in this market niche a viable opportunity for its long-term viability.

Considering all the aforementioned, the present research aims to perform a comprehensive comparison of the physical-mechanical properties of Canarian camel hair versus the widely acknowledged Merino sheep wool. The rationale behind this approach stems from the distinctive status of Merino wool as a benchmark in the textile industry due to its widespread use and recognition as a high-quality natural fiber. By contrasting camel fibers, representing an exotic and less explored resource, with Merino wool, we aim to uncover nuanced differences in the properties of the two fibers, shedding light on the specific qualities that make camel hair distinctive. This approach aligns with the research-based requirements for textile industry to foster innovation and diversification through the comparison of diverse fibers to understand their unique qualities and applications. The results will serve as a robust foundation for specifically advocating the revalorization of camel hair within the textile industry. In turn, entrepreneurs and camel breeders may be presented with an alternative niche that camel hair could competitively cover.

Material and methods

Animal sample

Clumps of camel outer hair were gathered by shearing from 139 Canarian dromedaries (77 males and 62 females; aged between 3 months and 35 years old; Table 1) during the molting season (Babu 2015). Harizi et al. (2007) highlight dromedary underhair as one of the finest fibers while noting that camel guard hair surpasses it in elongation and energy for rupture. This unique mechanical advantage positions camel guard hair as more promising for textile applications. Additionally, Knecht (2012) and Tridico (2015) emphasize the significance of guard hair in differentiating animal species. The samples were collected from six different body regions (shoulder, hump, belly, rump, tail dock, and tail skirt) per dromedary camel (Figure 1). These areas were chosen because the density of the hair coat is higher (Bhakat 2019); hence, larger samples can be obtained. Sampled dromedary camels were raised in semi-extensive conditions and fed identical diet.

Figure 1. Anatomical regions from which the dromedary camel hair and sheep wool samples were collected.

Table 1. Age group classification of dromedary camels and sheep.

Animal species	Group 1 Prepuber	Group 2 Sexually mature	Group 3 Reproductively senescent	Source of information
Dromedary camel	<3 years	3–15 years	>15 years	(Al-Qarawi et al. 2000, 2001)
Sheep	<6 months	6 months − 6 years	>6 years	(Nieto et al. 2013; Mysterud et al. 2002)

Merino sheep wool from 395 animals (214 males and 181 females; aged between 3 months and 5 years old; Table 1) was clipped with curved scissors from an area of 6 cm² during the first shearing. The anatomical regions from which the wool samples were collected are the shoulder, belly, and rump (Figure 1). For 55 adult animals, the fibers from each region were analyzed independently as well as a blended sample of them. The rest of the animals’ wool samples were analyzed once blended. Merino sheep that underwent sampling were bred in semi-extensive environments and fed identical diet.

Sample preparation and physical-mechanical analysis

Once obtained and before physical-mechanical analysis, camel hair and sheep wool samples were scoured with a mix of solid surfactant (hexane) and hot water (Allafi et al. 2022) to discard the animal grease and the dirty materials associated with the secretion of sweat glands and environmental pollution.

After scouring, fibers were dried at ambient temperature and analyzed using an optical fiber diameter measurement analysis (OFDA 2000, McLaughlin (2000)). A minimum of 1,500 fibers were measured and averaged for each sample. The average values for the temperature and relative humidity at the time of fiber measurement were 25°C and 45.7%, respectively.

Parameter description

Fiber parameters measured by the optical fiber diameter analyzer are listed in Table 2.

Table 2. Set of fiber parameters quantified by using an optical fiber diameter analysis and mean values per each parameter and species (Canarian camel and Merino sheep).

Parameter	Abbreviation	Unit of measurement	Mean value
			Canarian camel	Merino sheep
Average fiber diameter	meanD	μm	40.76	21.42
Standard deviation of fiber diameter	SDD	μm	13.43	3.97
Standard deviation expressed as a % of the sample’s average diameter	CVD	%	35.37	18.79
Difference between the top 5% of fibers in the histogram and the average fiber diameter	CEM	μm	24.36	7.06
Percentage of fibers less than or equal to 30 microns	CF	%	41.23	96.86
Millimetres from the tip to the finest point in the staple. It is an indicator for the “point of break”	fiFromTip	mm	25.43	24.45
Percentage of fibers in a sample less than 15 microns	%<15 μm	%	2.05	5.93
Maximum diameter along the staple	maxD	μm	41.96	23.67
Minimum diameter along the staple	minD	μm	34.56	14.77
Mean diameter at the base of the fiber	baseD	μm	37.76	15.39
Standard deviation of fiber diameter along the staple	SD Along Profile	μm	2.25	0.64
Standard deviation of fiber diameter across the staple	SD Across	μm	11.64	2.6
Spin fineness	spinF	μm	40.37	20.47
Average fiber curvature	meanC	Degrees per millimeter	23.24	114.71
Standard deviation of fiber curvature	SDC	Degrees per millimeter	24.98	74.94
Length of the relaxed staple	length staple	mm	40.71	58.23
Fat cover correction factor	GCF	μm	0	0
Total percentage area of blobs. It is an objective measure of fiber cleanliness	All blob%	%	1.87	0.78
Total percentage area of large blobs	large blob%	%	1.02	0.27
Total percentage area of small blobs	small blob%	%	0.85	0.51
Fibre diameter variation along about 200 μm of the snippet	sdD along 200 μm	μm	0.88	1
Minimum diameter along about 200 μm of the snippet	minD along 200 μm	μm	40.33	20.2
Number of fibers measured for diameter	numD	Number of fibers	2689.9	3137.3
Density of fibers on the slide	density	%	32.37	33.59
Standard deviation of density	denseSD	%	24.71	25.11
Current temperature during fiber analysis	temp C	ºC	26.12	23.51
Current air humidity during fiber analysis	RH%	%	43.85	48.16
Number of video images analyzed in the run	numFields	Number of video images	1684.47	1486.52
Time taken by the measurement	tmTaken	Seconds	10	10
Sharpness of fiber images	Focus	μm	10.19	11.14
Standard deviation of focus	focusSD	mm	3.38	3.35
Number of fibers out of focus	outFocus	Number of fibers	7.16	51.9
Number of fibers outside accepted light range	outLight	Number of fibers	0	0
Horizontal image width	horWidth	μm	0	0
Camera dark level at start	darkSt	Lumens %	11.88	11.94
Camera dark level in measure	dark	Lumens %	10	10
Standard deviation of camera dark level	darkSD	Lumens %	0	0
Minimum gray level inside fiber	min	ISO	15.81	20.69
Standard deviation of minimum gray level inside fiber	minSD	ISO	3.69	4.92
Light level next to fiber	light	nm wavelength	103.59	101.35
Standard deviation of light level next to fiber	lightSD	nm wavelength	1.14	1.52
Maximum light level next to fiber	max	nm wavelength	108.04	110.75
Standard deviation of maximum light level next to fiber	maxSD	nm wavelength	2.71	2.85
Camera noise standard deviation	noiseSD	EMVA Standard 1288	0	0
Microsecond flash length	Flash	microsecond	19.47	20.61
Home error in across stepper motor	hmErAc	degree	−0.42	−0.46
Home error in in out stepper motor	hmErIO	degree	−0.24	−0.27
Fibre density in OFDA4000 beard	Dense0 beard	mg/mm	0	0
Uniformity of fiber density in beard	Dense0 uniformity beard	mg/mm	0	0
Percentage of fibers hanging out in OFDA4000 beard	Pcnt hang 5 mm	%	0	0
Total number of runs since the instrument was built	totalRunCtr (12128.58)	Number of runs	14032.91	9592.11
Counter of the number of runs since the “Zero job counter” menu item was selected	jobRunCtr (4722.66)	Number of runs	5157.21	3595.13
Level of focus motor	focusMM	mm	1.42	1.24

Statistical analysis

A discriminant canonical analysis was performed in the present study to develop a tool that evaluates linear combinations of physical-mechanical hair/wool quality-related traits able to determine within and between population clustering patterns across species (Canarian camel and Merino sheep) and age groups (prepuber, sexually mature and reproductively senescent), following the methodology on González Ariza et al. (2021).

Results

Statistical analysis

Discriminant canonical analysis model reliability

After multicollinearity analyses, only the variables of light, maxSD, sdD along 200um, %<15um, fiFromTip, spinF, SD Along Profile, numD, densSD, minSD, CEM, lightSD, baseD, large blob%, and length staple were retained in the discriminant canonical analyses (VIF values < 5; Table 3).

Table 3. Multicollinearity analysis of physical-mechanical hair/wool quality related traits in Canarian camel and Merino sheep to discard for redundant variables.

Statistic	Tolerance (1-R^2)	VIF (1/Tolerance)
light	0.6342	1.5768
maxSD	0.5874	1.7024
sdD along 200um	0.5258	1.9019
%<15um	0.4901	2.0406
fiFromTip	0.4783	2.0906
spinF	0.4107	2.4349
SD Along Profile	0.3700	2.7024
numD	0.3675	2.7213
densSD	0.3571	2.8006
minSD	0.3241	3.0850
CEM	0.2830	3.5336
lightSD	0.2732	3.6606
baseD	0.2529	3.9542
large blob%	0.2524	3.9617
length staple	0.2225	4.4935

Interpretation thumb rule: VIF ≥ 5 (highly correlated); 1 < VIF < 5 (moderately correlated); VIF = 1 (not correlated).

A significant Pillai’s trace criterion (Pillai’s trace criterion: 2.5160; df1: 120; df2: 11720; p < .0001) determined the validity of the discriminant canonical analysis. Significant discriminant abilities were reported for six out of the eight functions revealed after the discriminant analysis, as reported in Table 4. The discriminatory power of the F1 function was high (eigenvalue of 11.21; Figure 2) with 99.91% of the variance significantly explained by F1, F2, F3, F4, F5, and F6.

Figure 2. Canonical variable functions and their percentages of self-explained and cumulative variance.

Table 4. Canonical discriminant analysis efficiency parameters to determine the significance of each canonical discriminant function.

Function	Canonical Correlation	Test of Function (s)	Wilks’ Lambda	Chi-square	df	Sig.
F1	0.9582	1 through 8	0.004	7034.445	120	<0.0001
F2	0.9123	2 through 8	0.054	3793.908	98	<0.0001
F3	0.6458	3 through 8	0.33	1439.932	78	<0.0001
F4	0.4626	4 through 8	0.623	613.23	60	<0.0001
F5	0.2893	5 through 8	0.834	236.36	44	<0.0001
F6	0.1860	6 through 8	0.931	92.605	30	<0.0001
F7	0.1016	7 through 8	0.979	28.187	18	0.059
F8	0.0769	8	0.993	9.496	8	0.302

Canonical coefficients, loading interpretation, and spatial representation

The different variables studied in this research were ranked according to their discriminating ability. A test of equality of group means of physical-mechanical hair/wool quality related traits was used, as shown in Table 5. A better discriminating power is indicated by greater values of F and, consequently, lower values of Wilks’ Lambda. The present analysis revealed that all physical-mechanical hair/wool quality related traits significantly contributed (p < .0001) to the discriminant functions.

Table 5. Results for the tests of equality of group means to test for difference in the means across sample groups once redundant variables have been removed.

Variable	Wilks’ Lambda	F	df1	df2	P-value	Rank
CEM	0.2122	683.0005	8	1472	<0.0001	1
large blob%	0.2266	628.1749	8	1472	<0.0001	2
lightSD	0.4544	220.9554	8	1472	<0.0001	3
baseD	0.4662	210.6743	8	1472	<0.0001	4
%<15um	0.5076	178.4672	8	1472	<0.0001	5
light	0.5416	155.7634	8	1472	<0.0001	6
minSD	0.5715	137.9877	8	1472	<0.0001	7
numD	0.7243	70.0222	8	1472	<0.0001	8
length staple	0.7311	67.6842	8	1472	<0.0001	9
sdD along 200um	0.7876	49.6291	8	1472	<0.0001	10
densSD	0.7917	48.4095	8	1472	<0.0001	11
SD Along Profile	0.8107	42.9770	8	1472	<0.0001	12
maxSD	0.8117	42.6957	8	1472	<0.0001	13
fiFromTip	0.8318	37.1998	8	1472	<0.0001	14
spinF	0.8644	28.8751	8	1472	<0.0001	15

Standardized discriminant coefficients measure the relative weight of each trait across the established discriminant functions (Figures 3 and 4). The two most relevant functions were used to depict a standardized discriminant coefficient biplot, which captures the highest fraction of data variability (Figure 4). Variables whose vector extends further beyond the origin more relevantly contribute to the F1 and F2 discriminant functions.

Figure 3. Discriminant coefficients for physical-mechanical hair/wool quality related traits in Canarian camel and Merino sheep in each canonical discriminant function. Each bar represents the relative weights (loadings) of each particular trait across the six significant discriminant functions evidenced by the discriminant canonical analysis.

Figure 4. Vector plot for discriminant loadings for the traits considered in discriminant analysis.

In Figure 5, centroids from different species and age groups considered in this study are represented. The relative position of each centroid was determined by substituting the mean value for the observations depicted in the two first discriminant functions (F1 and F2).

Figure 5. Territorial map depicting the centroids of the different observations considered in the discriminant canonical analysis sorted across species/breeds and age groups.

Mahalanobis distances across species and age groups were represented in a cladogram (Figure 6). Male prepuber sheep was the most distant age group when compared to the rest. A close connection between Sexually Mature Male and Female Sheep and Sexually Mature and Reproductively Senescent Male Camels was evidenced. Male Prepuber Camels are closer to Female Camels (even if they are reproductively senescent or sexually mature) than Camel Males, with Female Prepuber Camels being the most distant from the rest of the age groups within the Canarian camel cluster.

Figure 6. Cladogram constructed from Mahalanobis distances across species/breeds and age groups.

Data mining CHAID decision tree

The underlying basis for these classification patterns was represented in Supplementary Table S1, after the evaluation of the data mining CHAID decision tree obtained from the chi-square dissimilarity matrix. In these regard, Supplementary Table S2 describes data mining CHAID differential criteria across species, sex, and age groups (Significant differences were found at χ2 < 0.05). The only significant differences reported across sexes and age groups were found for the variables numD, minSD, denseSD, based, large blob %, CEM, light, lightSD, maxSD, fiFromtip, length staple, sdD along 200um, %<15um

spinF and SD Along Profile.

Discriminant function cross-validation

Cross-validation reported that 71.9% of original grouped cases correctly were classified, while 69.9% of cross-validated grouped cases were correctly classified. These results supported the robustness of the results obtained and the validity of the conclusions drawn from them. A Press’ Q value of 4271.52 (n = 1481; n’ = 955; K = 9) was computed. Thus, predictions can be considered better than chance at 95% (Chan 2005).

Discussion

In general, the variables that best discriminate between the fibers belonging to both species, through the particular examples of the Canarian camel breed and Merino Sheep, are those related to the diameter, density, length, their ability to reflect light, and residual dirt that is present. Among these attributes, we found characteristics of marked importance for fibers to present enough strength for industrial processing, that is, for fiber breakage to be minimized given the resultant degradation percentage, which is known to affect both the quality of the final fabrics and the opportunities for their commercialization (Peña, Poma Gutiérrez, and Purroy Unanua 2013). Moreover, the vast majority of the variables directly tied to the optical analyzer’s performance did not have any discriminatory potential. This confirms for the first time that OFDA serves as a dependable and precise technical instrument for examining the quality of less-explored animal-origin fibers like camel hair.

The diameter at the base of the fiber is greater for camel hair, with higher mean values being observed for adult males (>55.0 microns) when compared to females (25.06–55.00 microns) of the same age class. This variable is closely related to the volume of the primary follicles, from which the raw guard coat grows (Ansari-Renani et al. 2010), and to the volume of the follicular papilla found within this type of follicle (Burns and Clarkson 1949).

The higher the density of follicles, the greater the cellular volume and the secretory activity of the follicular papilla. Such physiological mechanisms are found to be linearly and positively related to both the size of the hair shaft produced (van Scott and Ekel 1958) and the hair’s general growth (Matsuzaki and Yoshizato 1998; Robinson et al. 2001).

In this context, Hekal (2014) reported the mean internal diameter of the primary follicle and the mean diameter of the guard hair fiber in Maghrabi and Sudani African camel breeds to be 75.01 µ and 45.5 µ, respectively, values in the range of what was found for the Canary camel, although these may be located at the lower end of the aforementioned range, which makes them approach the values found for Merino sheep in our study.

In these regard, for sheep wool, values reported in the literature are lower, with an average value of 23 µ (McCloghry, Brown, and Uphill 1997), which is set at the minimum values found for Canary she-camels. These may derive from the fact that hair from Canary camels was already used for the fabrication of clothes such as those used in tents, as carpets, or cloaks, hence a certain interest in the mass selection for better coats could be presumed.

Our study reported mean fiber diameters of 40.76 µm for camels and 21.42 µm for sheep wool, respectively. These results are confirmed by the CEM (Coarse Edge Micron) variable. This suggests that the percentage of fibers less than 15 microns in diameter, is lower in camels when compared to sheep, as was also confirmed in the present study.

In parallel, higher mean fiber length values were reported when sheep wool (58.23 mm) was compared to camel hair (40.71 mm). Such a finding would contrast with that reported by Banamali et al. (2000), who obtained fiber lengths greater than 60 mm when comparing Bikaneri, Jaisalmeri and Kachchhi camel breeds in India. However, our results are in line with the general conclusions stated by Bhakat (2019) for camels and Valera et al. (2002) for Merino sheep. These authors point out that the diameter and length of these fibers increase with age, and thus the skin coarseness goes up, a correlation that can be corroborated within our dataset.

When sexes are compared, in both species, males present slightly lower fiber diameter and length, as stated by previous studies (Baba et al. 2020; Banamali et al. 2000). In any case, the significance of the effects of age and sex on the physical attributes of the fibers varies between investigations, which would indicate that the fiber attributes are not only influenced by genetic factors but also by other environmental factors (diet, health status, and/or climatology). This quantitative variability between breeds and genetic lines for these traits indicates scope for further improvement through selective breeding.

The diameter of the fiber is susceptible to variation along its length (Hutchinson and Thompson 1997). This variability, as shown in our data (sdD along 200 µm, SD Along profile, and denSD) and as would be expected according to the aforementioned influence of the size and secretion of the follicular papilla on general hair growth, is critical to sheep wool performance. Although the standard deviation of fiber diameter along the staple is higher for camel hair, as would be expected considering its superior mean diameter, sheep wool is a fiber with greater irregularity in its diameter along short segments (200 um) of the fiber. This characteristic could be directly correlated to the smaller relative distance from the tip of the fiber to the thinnest point and thus to a potential break point in sheep wool.

Although both fibers are keratin fibers, the spatial overlapping of the scales along the cuticle sheath and the cystine content in the protein chairs differ between camel hair and sheep wool. In the first case, Tridico (2009) identified, by low-power microscopy, that camel hair is quite regular in outline, exhibits a uniform diameter along its length, and the cuticular scale edges project so very slightly from the hair shaft that its profile appears almost straight. Concerning the cystine content, evidence shows that camel hair, both the inner and outer coat, hardly contains non-cystine, sulfur-containing compounds (Rimington 1931).

In this connection, several implications for textile production derived from these exotic fiber particularities will be discussed. As both fiber diameter and its variation play an important role in fiber processing (Helal 2015), the homogeneity of camel fibers should be a value-added characteristic for textile manufacturing since it would provide major resistance to different processing techniques in the textile industry (X. Wang 2000). In those areas where there is a smaller fiber diameter (quantitatively, the number of areas with a low average diameter is higher in sheep’s wool), the tensile resistance is lower (Bolormaa, Yves Drean, and Enkhtuya 2008) and, consequently, the probability of fiber breakage is higher (Li et al. 2011). Closely linked, the low coefficient of friction of camel hair due to its particular homogeneous, straight scale overlapping along its surface (Shakyawar, Patni, and Gupta 2007) confers special slippery and smooth properties and thus comfortable tactile perception of textile fabrics (Ramalho, Szekeres, and Fernandes 2013).

In the matter of cysteine content, Campbell, Whiteley, and Gillespie (1972) found a positive relationship between this characteristic and curvature in wool. That is, the greater the content of cystine, the greater the crimping rate of wool. On the one hand, such a trait could be the main reason that explains the greater mean number of wool fibers measured (numD) in the present study, since natural crimping makes fiber coil and thus confers extra resilience to fibers when they are intended to be separated for optical analysis (Miao and Gordon 2018).

Given that the concentration of cysteine in camel hair remains low in comparison with sheep wool, the respective crimping rate is therefore expected to be low as well. This lower rate can be additionally explained based on their high and uniform hair fiber diameter (Nissimov and Baran Das Chaudhuri 2014) and thus higher elastic modulus (Tang et al. 2016). All in all, this set of reasons is further supported by the notably higher spin fineness of camel hair. This characteristic provides an estimate of the performance of the sample when it is converted into yarn. Its estimate comes from the combination of the mean fiber diameter and its coefficient of variation (Quispe-Ccasa et al. 2020).

For ecology-based evolutionary reasons, the distinguished attributes of camel hair may further brighten the multiple adaptations of these animals to live in arid land areas where they are constantly exposed to abrasion by sand at the same time they have to deal with extreme temperatures. The hair coats of desert-living mammals might have evolved to be as strong and straight as possible to provide a non-bendable, smooth surface for light reflection since this physical property is negatively affected as long as fiber curvature increases (Wortmann, Schulze zur Wiesche, and Bourceau 2004). Through NIR (Near Infrared Spectroscopy) analysis, Chen, Lin, and Tan (2019) confirmed a higher light scattering for camel hair fibers and correlated this property with fiber diameter and homogeneous surface in this species’ hair.

Our results support this hypothesis, as the ability to reflect light is higher for camel hair, as well as the variability found for this property is greater in sheep, the latter in turn related to its also greater variability for the diameter along the fiber. According to Young, Hale, and Mechels (1993), the absorption and scattering of light energy within an optical fiber (gray-scale analysis) are dependent both on the homogeneity of the circularity of the outer or cladding diameter of the fiber and the natural impurities along the fiber length.

This greater ability to reflect light, but what’s more important, the presence of a medulla inside the fiber, may provide important benefits for thermal insulation not only to camels in their natural habitat but also to textile products made with their fibers. Indeed, the mere presence of inner medullation in the camel fibers reduces their heat transmittance (Øritsland and Ronald 1978), being this correlation is superior as the diameter of the fiber increases its value (Moore, Blache, and Maloney 2011). Within a practical framework in which camel breeding programs may become patent for functional selection on fiber fineness, we propose to first establish a minimum value of fiber diameter to be respected in all cases to avoid negative effects on the camel’s thermal balance and thus their general health status and production performance. For example, Lakshmanan, Jose, and Chakraborty (2016) established camel-origin luxury fibers for the fashion industry about 18–26 in diameter and 30–120 mm in length, but no evaluation of the heat transmittance effect for these values was done. Thus, extended research to determine the critical value of this camel hair fiber attribute is urgently needed for animal welfare procurement.

In line with the aforementioned and considering the scene of an increasing technification of camel-rearing systems due to the emerging interests in this species’ production potentialities (Iglesias Pastrana, Navas González, Ciani, Barba Capote, et al. 2020), it is expected camels to be reared under controlled environments. The improved technical assistance and supervision may enhance not only their welfare but also the preventive care that the animals receive. Hence, among others, the percentage of fiber impurities (% large blob) that are known to affect the efficiency of technological operations at textile manufacturing should be minimized as much as possible to make camel’s hair cleaner and healthier. This specific parameter can be related to both the presence of contaminants and other fiber damage. Therefore, a further comprehensive investigation of its meaning for camel hair fibers through conventional microscopy may aid at refining camel selection criteria for fiber quality-related traits, and enlarging the textile applications and market opportunities for these specific animal-origin fibers. According to our results, this percentage is higher in Canary camels than in Merino ewes, which could be explained based on the extensive or semi-extensive regime and a lower degree of technification of most camel farms (McGregor 2018). Regarding the influence of sex, the value of this variable is higher in males, which could be directly and proportionally related to the greater amount (kg) of fiber produced by this sex (Babu 2015).

Eventually, by the increasing technological development of the textile industry and the ongoing reinforcing of camel breeding programs for the improved production of textile fibers, a preliminary screening of the fibers at their reception in the industry could help with their differential classification for different applicative purposes. That is the case, for example, those fibers that do not meet the preferred requirements for a certain product or textile market, but that would have applicability both in engineering and bioengineering due to their insulating properties, with special attention to sustainability issues (Parlato and Porto 2020).

Additionally, while this paper focuses on camel guard hair, future research is encouraged to explore under or down hair using a similar methodology. This way, the further the knowledge on the differences between camel down and guard hair, the greater the potential of valorization of market opportunities for camel hair fibers (Xiao and Hu 2016).

Conclusions

Despite the slightly shorter length of Canarian camel hair when compared to Merino sheep wool, the general performance of camel fibers in terms of fiber diameter and light reflection capacity can be a source of benefit in the textile industry. The greater diameter at the base and the spin fineness of camel hair gives it greater resistance to industrial processing in addition to providing a softer sensation to the touch. The ability to reflect light and the internal medullation make camel-origin fiber a valuable textile material for the lightness and heat-insulating properties of final textile products when compared to other widespread fiber such as Merino wool. Fiber impurities must be exhaustively controlled both on-farm and industrial settings to ensure a high percentage of fiber suitable for profitable processing. That being so, it is decisive that farmers become conscious of the potential of camel hair as an income source and that textile stakeholders become aware of the characteristic attributes of this exotic animal fiber.

Highlights

The greater the diameter at the base and the spin fineness in camel hair, the greater its resistance to industrial processing and the softer sensation to the touch.

The ability to absorb/reflect light and the internal medullation provides camel hair with enhanced heat-insulating properties.

Residual dirt must be exhaustively controlled to ensure a high percentage of camel fibers suitable for profitable processing.

Acknowledgments

The authors would like to thank ‘Aires Africanos’ Aires Africanos” Eco-tourism Company, Oasis Park Fuerteventura and ‘Camelus’ Camellos de Almería, for their direct technical help and assistance.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15440478.2024.2326920.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, F.J.N.G., upon reasonable request.

Additional information

Funding

The present research was carried out in the financing framework of the international project CA.RA.VA.N - “Toward a Camel Transnational Value Chain” (Reference APCIN-2016-00011-00-00) and during the coverage period of a predoctoral contract (FPU Fellowship) funded by the Spanish Ministry of Science and Innovation and a Ramón y Cajal Post-Doctoral Contract with the reference MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR.